Apparatus and method for dynamic provisioning, quality of service, and prioritization in a graphics processor

ABSTRACT

An apparatus and method for dynamic provisioning, quality of service, and prioritization in a graphics processor. For example, one embodiment of an apparatus comprises a graphics processing unit (GPU) comprising a plurality of graphics processing resources; slice configuration hardware logic to logically subdivide the graphics processing resources into a plurality of slices; and slice allocation hardware logic to allocate a designated number of slices to each virtual machine (VM) of a plurality of VMs running in a virtualized execution environment, the slice allocation hardware logic to allocate different numbers of slices to different VMs based on graphics processing requirements and/or priorities of each of the VMs.

BACKGROUND Field of the Invention

This invention relates generally to the field of computer processors.More particularly, the invention relates to an apparatus and method fordynamic provisioning, quality of service (QoS) and prioritization in agraphics processor.

Description of the Related Art

Rapid advances have recently taken place in graphics processor unit(GPU) virtualization. Virtualized graphics processing environments areused, for example, in the media cloud, remote workstations/desktops,Interchangeable Virtual Instrumentation (IVI), rich clientvirtualization, to name a few. Certain architectures perform full GPUvirtualization through trap-and-emulation to emulate a full-featuredvirtual GPU (vGPU) while still providing near-to-native performance bypassing through performance-critical graphics memory resources.

With the increasing importance of GPUs in servers to support 3D, mediaand GPGPU workloads, GPU virtualization is becoming more widespread. Howto virtualize GPU memory access from a virtual machine (VM) is one ofthe key design factors. The GPU has its own graphics memory: eitherdedicated video memory or shared system memory. When system memory isused for graphics, guest physical addresses (GPAs) need to be translatedto host physical addresses (HPAs) before being accessed by hardware.

There are various approaches for performing translation for GPUs. Someimplementations perform translation with hardware support, but the GPUcan be passed-through to one VM only. Another solution is a softwareapproach which constructs shadow structures for the translation. Forinstance, shadow page tables are implemented in some architectures suchas the full GPU virtualization solution mentioned above, which cansupport multiple VMs to share a physical GPU.

In some implementations, the guest/VM memory pages are backed by hostmemory pages. A virtual machine monitor (VMM) (sometimes called a“Hypervisor”) uses extended page tables (EPT), for example, to map froma guest physical address (PA) to a host PA. Many memory sharingtechnologies may be used, such as Kernel Same page Merging (KSM).

KSM combines pages from multiple VMs with the same content, to a singlepage with write protection. That is to say, if a memory page in VM1(mapping from guest PA1 to host PA1), has the same contents as anothermemory page in VM2 (mapping from guest PA2 to host PA2), may use onlyone host page (say HPA_SH) to back the guest memory. That is, both guestPA1 of VM1 and PA2 of VM2 are mapped to HPA_SH with write protection.This saves the memory used for the system, and is particularly usefulfor read-only memory pages of the guest such as code pages, and zeropages. With KSM, copy-on-write (COW) technology is used to remove thesharing once a VM modifies the page content.

Mediate pass through is used in virtualization systems for deviceperformance and sharing, where a single physical GPU is presented asmultiple virtual GPU to multiple guests with direct DMA, while theprivileges resource accesses from guests are still trap-and-emulated. Insome implementations, each guest can run the native GPU driver, anddevice DMA goes directly to memory without hypervisor intervention.

BRIEF DESCRIPTION OF THE DRAWINGS

A better understanding of the present invention can be obtained from thefollowing detailed description in conjunction with the followingdrawings, in which:

FIG. 1 is a block diagram of an embodiment of a computer system with aprocessor having one or more processor cores and graphics processors;

FIG. 2 is a block diagram of one embodiment of a processor having one ormore processor cores, an integrated memory controller, and an integratedgraphics processor;

FIG. 3 is a block diagram of one embodiment of a graphics processorwhich may be a discreet graphics processing unit, or may be graphicsprocessor integrated with a plurality of processing cores;

FIG. 4 is a block diagram of an embodiment of a graphics-processingengine for a graphics processor;

FIG. 5 is a block diagram of another embodiment of a graphics processor;

FIG. 6 is a block diagram of thread execution logic including an arrayof processing elements;

FIG. 7 illustrates a graphics processor execution unit instructionformat according to an embodiment;

FIG. 8 is a block diagram of another embodiment of a graphics processorwhich includes a graphics pipeline, a media pipeline, a display engine,thread execution logic, and a render output pipeline;

FIG. 9A is a block diagram illustrating a graphics processor commandformat according to an embodiment;

FIG. 9B is a block diagram illustrating a graphics processor commandsequence according to an embodiment;

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system according to an embodiment;

FIG. 11 illustrates an exemplary IP core development system that may beused to manufacture an integrated circuit to perform operationsaccording to an embodiment;

FIG. 12 illustrates an exemplary system on a chip integrated circuitthat may be fabricated using one or more IP cores, according to anembodiment;

FIG. 13 illustrates an exemplary graphics processor of a system on achip integrated circuit that may be fabricated using one or more IPcores;

FIG. 14 illustrates an additional exemplary graphics processor of asystem on a chip integrated circuit that may be fabricated using one ormore IP cores

FIG. 15 illustrates an exemplary graphics processing system;

FIG. 16 illustrates an exemplary architecture for full graphicsvirtualization;

FIG. 17 illustrates an exemplary virtualized graphics processingarchitecture including virtual graphics processing units (vGPUs);

FIG. 18 illustrates one embodiment of a virtualization architecture withan IOMMU;

FIG. 19 illustrates one embodiment in which graphics processing isperformed on a server;

FIG. 20 illustrates one embodiment in which slices are dynamicallyprovisioned to virtual machines;

FIG. 21 illustrates a method in accordance with one embodiment of theinvention;

FIG. 22 illustrates one embodiment with a plurality of statisticscounters and a report generator;

FIG. 23 illustrates a method in accordance with one embodiment of theinvention;

FIG. 24 illustrates one embodiment with a plurality of producers andconsumers;

FIG. 25 illustrates an exemplary arrangement including a register frontcopy and register back copy;

FIG. 26 illustrates a method in accordance with one embodiment of theinvention;

FIG. 27 is a block diagram illustrating a computer system configured toimplement one or more aspects of the embodiments described herein;

FIG. 28A-28D illustrate a parallel processor components, according to anembodiment;

FIGS. 29A-29B are block diagrams of graphics multiprocessors, accordingto embodiments;

FIG. 30A-30F illustrate an exemplary architecture in which a pluralityof GPUs are communicatively coupled to a plurality of multi-coreprocessors; and

FIG. 31 illustrates a graphics processing pipeline, according to anembodiment.

DETAILED DESCRIPTION

In the following description, for the purposes of explanation, numerousspecific details are set forth in order to provide a thoroughunderstanding of the embodiments of the invention described below. Itwill be apparent, however, to one skilled in the art that theembodiments of the invention may be practiced without some of thesespecific details. In other instances, well-known structures and devicesare shown in block diagram form to avoid obscuring the underlyingprinciples of the embodiments of the invention.

Exemplary Graphics Processor Architectures and Data Types SystemOverview

FIG. 1 is a block diagram of a processing system 100, according to anembodiment. In various embodiments the system 100 includes one or moreprocessors 102 and one or more graphics processors 108, and may be asingle processor desktop system, a multiprocessor workstation system, ora server system having a large number of processors 102 or processorcores 107. In one embodiment, the system 100 is a processing platformincorporated within a system-on-a-chip (SoC) integrated circuit for usein mobile, handheld, or embedded devices.

An embodiment of system 100 can include, or be incorporated within aserver-based gaming platform, a game console, including a game and mediaconsole, a mobile gaming console, a handheld game console, or an onlinegame console. In some embodiments system 100 is a mobile phone, smartphone, tablet computing device or mobile Internet device. Dataprocessing system 100 can also include, couple with, or be integratedwithin a wearable device, such as a smart watch wearable device, smarteyewear device, augmented reality device, or virtual reality device. Insome embodiments, data processing system 100 is a television or set topbox device having one or more processors 102 and a graphical interfacegenerated by one or more graphics processors 108.

In some embodiments, the one or more processors 102 each include one ormore processor cores 107 to process instructions which, when executed,perform operations for system and user software. In some embodiments,each of the one or more processor cores 107 is configured to process aspecific instruction set 109. In some embodiments, instruction set 109may facilitate Complex Instruction Set Computing (CISC), ReducedInstruction Set Computing (RISC), or computing via a Very LongInstruction Word (VLIW). Multiple processor cores 107 may each process adifferent instruction set 109, which may include instructions tofacilitate the emulation of other instruction sets. Processor core 107may also include other processing devices, such a Digital SignalProcessor (DSP).

In some embodiments, the processor 102 includes cache memory 104.Depending on the architecture, the processor 102 can have a singleinternal cache or multiple levels of internal cache. In someembodiments, the cache memory is shared among various components of theprocessor 102. In some embodiments, the processor 102 also uses anexternal cache (e.g., a Level-3 (L3) cache or Last Level Cache (LLC))(not shown), which may be shared among processor cores 107 using knowncache coherency techniques. A register file 106 is additionally includedin processor 102 which may include different types of registers forstoring different types of data (e.g., integer registers, floating pointregisters, status registers, and an instruction pointer register). Someregisters may be general-purpose registers, while other registers may bespecific to the design of the processor 102.

In some embodiments, processor 102 is coupled with a processor bus 110to transmit communication signals such as address, data, or controlsignals between processor 102 and other components in system 100. In oneembodiment the system 100 uses an exemplary ‘hub’ system architecture,including a memory controller hub 116 and an Input Output (I/O)controller hub 130. A memory controller hub 116 facilitatescommunication between a memory device and other components of system100, while an I/O Controller Hub (ICH) 130 provides connections to I/Odevices via a local I/O bus. In one embodiment, the logic of the memorycontroller hub 116 is integrated within the processor.

Memory device 120 can be a dynamic random access memory (DRAM) device, astatic random access memory (SRAM) device, flash memory device,phase-change memory device, or some other memory device having suitableperformance to serve as process memory. In one embodiment the memorydevice 120 can operate as system memory for the system 100, to storedata 122 and instructions 121 for use when the one or more processors102 executes an application or process. Memory controller hub 116 alsocouples with an optional external graphics processor 112, which maycommunicate with the one or more graphics processors 108 in processors102 to perform graphics and media operations.

In some embodiments, ICH 130 enables peripherals to connect to memorydevice 120 and processor 102 via a high-speed I/O bus. The I/Operipherals include, but are not limited to, an audio controller 146, afirmware interface 128, a wireless transceiver 126 (e.g., Wi-Fi,Bluetooth), a data storage device 124 (e.g., hard disk drive, flashmemory, etc.), and a legacy I/O controller 140 for coupling legacy(e.g., Personal System 2 (PS/2)) devices to the system. One or moreUniversal Serial Bus (USB) controllers 142 connect input devices, suchas keyboard and mouse 144 combinations. A network controller 134 mayalso couple with ICH 130. In some embodiments, a high-performancenetwork controller (not shown) couples with processor bus 110. It willbe appreciated that the system 100 shown is exemplary and not limiting,as other types of data processing systems that are differentlyconfigured may also be used. For example, the I/O controller hub 130 maybe integrated within the one or more processor 102, or the memorycontroller hub 116 and I/O controller hub 130 may be integrated into adiscreet external graphics processor, such as the external graphicsprocessor 112.

FIG. 2 is a block diagram of an embodiment of a processor 200 having oneor more processor cores 202A-202N, an integrated memory controller 214,and an integrated graphics processor 208. Those elements of FIG. 2having the same reference numbers (or names) as the elements of anyother figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such. Processor200 can include additional cores up to and including additional core202N represented by the dashed lined boxes. Each of processor cores202A-202N includes one or more internal cache units 204A-204N. In someembodiments each processor core also has access to one or more sharedcached units 206.

The internal cache units 204A-204N and shared cache units 206 representa cache memory hierarchy within the processor 200. The cache memoryhierarchy may include at least one level of instruction and data cachewithin each processor core and one or more levels of shared mid-levelcache, such as a Level 2 (L2), Level 3 (L3), Level 4 (L4), or otherlevels of cache, where the highest level of cache before external memoryis classified as the LLC. In some embodiments, cache coherency logicmaintains coherency between the various cache units 206 and 204A-204N.

In some embodiments, processor 200 may also include a set of one or morebus controller units 216 and a system agent core 210. The one or morebus controller units 216 manage a set of peripheral buses, such as oneor more Peripheral Component Interconnect buses (e.g., PCI, PCIExpress). System agent core 210 provides management functionality forthe various processor components. In some embodiments, system agent core210 includes one or more integrated memory controllers 214 to manageaccess to various external memory devices (not shown).

In some embodiments, one or more of the processor cores 202A-202Ninclude support for simultaneous multi-threading. In such embodiment,the system agent core 210 includes components for coordinating andoperating cores 202A-202N during multi-threaded processing. System agentcore 210 may additionally include a power control unit (PCU), whichincludes logic and components to regulate the power state of processorcores 202A-202N and graphics processor 208.

In some embodiments, processor 200 additionally includes graphicsprocessor 208 to execute graphics processing operations. In someembodiments, the graphics processor 208 couples with the set of sharedcache units 206, and the system agent core 210, including the one ormore integrated memory controllers 214. In some embodiments, a displaycontroller 211 is coupled with the graphics processor 208 to drivegraphics processor output to one or more coupled displays. In someembodiments, display controller 211 may be a separate module coupledwith the graphics processor via at least one interconnect, or may beintegrated within the graphics processor 208 or system agent core 210.

In some embodiments, a ring based interconnect unit 212 is used tocouple the internal components of the processor 200. However, analternative interconnect unit may be used, such as a point-to-pointinterconnect, a switched interconnect, or other techniques, includingtechniques well known in the art. In some embodiments, graphicsprocessor 208 couples with the ring interconnect 212 via an I/O link213.

The exemplary I/O link 213 represents at least one of multiple varietiesof I/O interconnects, including an on package I/O interconnect whichfacilitates communication between various processor components and ahigh-performance embedded memory module 218, such as an eDRAM module. Insome embodiments, each of the processor cores 202A-202N and graphicsprocessor 208 use embedded memory modules 218 as a shared Last LevelCache.

In some embodiments, processor cores 202A-202N are homogenous coresexecuting the same instruction set architecture. In another embodiment,processor cores 202A-202N are heterogeneous in terms of instruction setarchitecture (ISA), where one or more of processor cores 202A-202Nexecute a first instruction set, while at least one of the other coresexecutes a subset of the first instruction set or a differentinstruction set. In one embodiment processor cores 202A-202N areheterogeneous in terms of microarchitecture, where one or more coreshaving a relatively higher power consumption couple with one or morepower cores having a lower power consumption. Additionally, processor200 can be implemented on one or more chips or as an SoC integratedcircuit having the illustrated components, in addition to othercomponents.

FIG. 3 is a block diagram of a graphics processor 300, which may be adiscrete graphics processing unit, or may be a graphics processorintegrated with a plurality of processing cores. In some embodiments,the graphics processor communicates via a memory mapped I/O interface toregisters on the graphics processor and with commands placed into theprocessor memory. In some embodiments, graphics processor 300 includes amemory interface 314 to access memory. Memory interface 314 can be aninterface to local memory, one or more internal caches, one or moreshared external caches, and/or to system memory.

In some embodiments, graphics processor 300 also includes a displaycontroller 302 to drive display output data to a display device 320.Display controller 302 includes hardware for one or more overlay planesfor the display and composition of multiple layers of video or userinterface elements. In some embodiments, graphics processor 300 includesa video codec engine 306 to encode, decode, or transcode media to, from,or between one or more media encoding formats, including, but notlimited to Moving Picture Experts Group (MPEG) formats such as MPEG-2,Advanced Video Coding (AVC) formats such as H.264/MPEG-4 AVC, as well asthe Society of Motion Picture & Television Engineers (SMPTE) 421 M/VC-1,and Joint Photographic Experts Group (JPEG) formats such as JPEG, andMotion JPEG (MJPEG) formats.

In some embodiments, graphics processor 300 includes a block imagetransfer (BLIT) engine 304 to perform two-dimensional (2D) rasterizeroperations including, for example, bit-boundary block transfers.However, in one embodiment, 2D graphics operations are performed usingone or more components of graphics processing engine (GPE) 310. In someembodiments, GPE 310 is a compute engine for performing graphicsoperations, including three-dimensional (3D) graphics operations andmedia operations.

In some embodiments, GPE 310 includes a 3D pipeline 312 for performing3D operations, such as rendering three-dimensional images and scenesusing processing functions that act upon 3D primitive shapes (e.g.,rectangle, triangle, etc.). The 3D pipeline 312 includes programmableand fixed function elements that perform various tasks within theelement and/or spawn execution threads to a 3D/Media sub-system 315.While 3D pipeline 312 can be used to perform media operations, anembodiment of GPE 310 also includes a media pipeline 316 that isspecifically used to perform media operations, such as videopost-processing and image enhancement.

In some embodiments, media pipeline 316 includes fixed function orprogrammable logic units to perform one or more specialized mediaoperations, such as video decode acceleration, video de-interlacing, andvideo encode acceleration in place of, or on behalf of video codecengine 306. In some embodiments, media pipeline 316 additionallyincludes a thread spawning unit to spawn threads for execution on3D/Media sub-system 315. The spawned threads perform computations forthe media operations on one or more graphics execution units included in3D/Media sub-system 315.

In some embodiments, 3D/Media subsystem 315 includes logic for executingthreads spawned by 3D pipeline 312 and media pipeline 316. In oneembodiment, the pipelines send thread execution requests to 3D/Mediasubsystem 315, which includes thread dispatch logic for arbitrating anddispatching the various requests to available thread executionresources. The execution resources include an array of graphicsexecution units to process the 3D and media threads. In someembodiments, 3D/Media subsystem 315 includes one or more internal cachesfor thread instructions and data. In some embodiments, the subsystemalso includes shared memory, including registers and addressable memory,to share data between threads and to store output data.

Graphics Processing Engine

FIG. 4 is a block diagram of a graphics processing engine 410 of agraphics processor in accordance with some embodiments. In oneembodiment, the graphics processing engine (GPE) 410 is a version of theGPE 310 shown in FIG. 3. Elements of FIG. 4 having the same referencenumbers (or names) as the elements of any other figure herein canoperate or function in any manner similar to that described elsewhereherein, but are not limited to such. For example, the 3D pipeline 312and media pipeline 316 of FIG. 3 are illustrated. The media pipeline 316is optional in some embodiments of the GPE 410 and may not be explicitlyincluded within the GPE 410. For example and in at least one embodiment,a separate media and/or image processor is coupled to the GPE 410.

In some embodiments, GPE 410 couples with or includes a command streamer403, which provides a command stream to the 3D pipeline 312 and/or mediapipelines 316. In some embodiments, command streamer 403 is coupled withmemory, which can be system memory, or one or more of internal cachememory and shared cache memory. In some embodiments, command streamer403 receives commands from the memory and sends the commands to 3Dpipeline 312 and/or media pipeline 316. The commands are directivesfetched from a ring buffer, which stores commands for the 3D pipeline312 and media pipeline 316. In one embodiment, the ring buffer canadditionally include batch command buffers storing batches of multiplecommands. The commands for the 3D pipeline 312 can also includereferences to data stored in memory, such as but not limited to vertexand geometry data for the 3D pipeline 312 and/or image data and memoryobjects for the media pipeline 316. The 3D pipeline 312 and mediapipeline 316 process the commands and data by performing operations vialogic within the respective pipelines or by dispatching one or moreexecution threads to a graphics core array 414.

In various embodiments the 3D pipeline 312 can execute one or moreshader programs, such as vertex shaders, geometry shaders, pixelshaders, fragment shaders, compute shaders, or other shader programs, byprocessing the instructions and dispatching execution threads to thegraphics core array 414. The graphics core array 414 provides a unifiedblock of execution resources. Multi-purpose execution logic (e.g.,execution units) within the graphic core array 414 includes support forvarious 3D API shader languages and can execute multiple simultaneousexecution threads associated with multiple shaders.

In some embodiments the graphics core array 414 also includes executionlogic to perform media functions, such as video and/or image processing.In one embodiment, the execution units additionally includegeneral-purpose logic that is programmable to perform parallel generalpurpose computational operations, in addition to graphics processingoperations. The general purpose logic can perform processing operationsin parallel or in conjunction with general purpose logic within theprocessor core(s) 107 of FIG. 1 or core 202A-202N as in FIG. 2.

Output data generated by threads executing on the graphics core array414 can output data to memory in a unified return buffer (URB) 418. TheURB 418 can store data for multiple threads. In some embodiments the URB418 may be used to send data between different threads executing on thegraphics core array 414. In some embodiments the URB 418 mayadditionally be used for synchronization between threads on the graphicscore array and fixed function logic within the shared function logic420.

In some embodiments, graphics core array 414 is scalable, such that thearray includes a variable number of graphics cores, each having avariable number of execution units based on the target power andperformance level of GPE 410. In one embodiment the execution resourcesare dynamically scalable, such that execution resources may be enabledor disabled as needed.

The graphics core array 414 couples with shared function logic 420 thatincludes multiple resources that are shared between the graphics coresin the graphics core array. The shared functions within the sharedfunction logic 420 are hardware logic units that provide specializedsupplemental functionality to the graphics core array 414. In variousembodiments, shared function logic 420 includes but is not limited tosampler 421, math 422, and inter-thread communication (ITC) 423 logic.Additionally, some embodiments implement one or more cache(s) 425 withinthe shared function logic 420. A shared function is implemented wherethe demand for a given specialized function is insufficient forinclusion within the graphics core array 414. Instead a singleinstantiation of that specialized function is implemented as astand-alone entity in the shared function logic 420 and shared among theexecution resources within the graphics core array 414. The precise setof functions that are shared between the graphics core array 414 andincluded within the graphics core array 414 varies between embodiments.

FIG. 5 is a block diagram of another embodiment of a graphics processor500. Elements of FIG. 5 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 500 includes a ring interconnect502, a pipeline front-end 504, a media engine 537, and graphics cores580A-580N. In some embodiments, ring interconnect 502 couples thegraphics processor to other processing units, including other graphicsprocessors or one or more general-purpose processor cores. In someembodiments, the graphics processor is one of many processors integratedwithin a multi-core processing system.

In some embodiments, graphics processor 500 receives batches of commandsvia ring interconnect 502. The incoming commands are interpreted by acommand streamer 503 in the pipeline front-end 504. In some embodiments,graphics processor 500 includes scalable execution logic to perform 3Dgeometry processing and media processing via the graphics core(s)580A-580N. For 3D geometry processing commands, command streamer 503supplies commands to geometry pipeline 536. For at least some mediaprocessing commands, command streamer 503 supplies the commands to avideo front end 534, which couples with a media engine 537. In someembodiments, media engine 537 includes a Video Quality Engine (VQE) 530for video and image post-processing and a multi-format encode/decode(MFX) 533 engine to provide hardware-accelerated media data encode anddecode. In some embodiments, geometry pipeline 536 and media engine 537each generate execution threads for the thread execution resourcesprovided by at least one graphics core 580A.

In some embodiments, graphics processor 500 includes scalable threadexecution resources featuring modular cores 580A-580N (sometimesreferred to as core slices), each having multiple sub-cores 550A-550N,560A-560N (sometimes referred to as core sub-slices). In someembodiments, graphics processor 500 can have any number of graphicscores 580A through 580N. In some embodiments, graphics processor 500includes a graphics core 580A having at least a first sub-core 550A anda second sub-core 560A. In other embodiments, the graphics processor isa low power processor with a single sub-core (e.g., 550A). In someembodiments, graphics processor 500 includes multiple graphics cores580A-580N, each including a set of first sub-cores 550A-550N and a setof second sub-cores 560A-560N. Each sub-core in the set of firstsub-cores 550A-550N includes at least a first set of execution units552A-552N and media/texture samplers 554A-554N. Each sub-core in the setof second sub-cores 560A-560N includes at least a second set ofexecution units 562A-562N and samplers 564A-564N. In some embodiments,each sub-core 550A-550N, 560A-560N shares a set of shared resources570A-570N. In some embodiments, the shared resources include sharedcache memory and pixel operation logic. Other shared resources may alsobe included in the various embodiments of the graphics processor.

Execution Units

FIG. 6 illustrates thread execution logic 600 including an array ofprocessing elements employed in some embodiments of a GPE. Elements ofFIG. 6 having the same reference numbers (or names) as the elements ofany other figure herein can operate or function in any manner similar tothat described elsewhere herein, but are not limited to such.

In some embodiments, thread execution logic 600 includes a shaderprocessor 602, a thread dispatcher 604, instruction cache 606, ascalable execution unit array including a plurality of execution units608A-608N, a sampler 610, a data cache 612, and a data port 614. In oneembodiment the scalable execution unit array can dynamically scale byenabling or disabling one or more execution units (e.g., any ofexecution unit 608A, 608B, 608C, 608D, through 608N-1 and 608N) based onthe computational requirements of a workload. In one embodiment theincluded components are interconnected via an interconnect fabric thatlinks to each of the components. In some embodiments, thread executionlogic 600 includes one or more connections to memory, such as systemmemory or cache memory, through one or more of instruction cache 606,data port 614, sampler 610, and execution units 608A-608N. In someembodiments, each execution unit (e.g. 608A) is a stand-aloneprogrammable general purpose computational unit that is capable ofexecuting multiple simultaneous hardware threads while processingmultiple data elements in parallel for each thread. In variousembodiments, the array of execution units 608A-608N is scalable toinclude any number individual execution units.

In some embodiments, the execution units 608A-608N are primarily used toexecute shader programs. A shader processor 602 can process the variousshader programs and dispatch execution threads associated with theshader programs via a thread dispatcher 604. In one embodiment thethread dispatcher includes logic to arbitrate thread initiation requestsfrom the graphics and media pipelines and instantiate the requestedthreads on one or more execution unit in the execution units 608A-608N.For example, the geometry pipeline (e.g., 536 of FIG. 5) can dispatchvertex, tessellation, or geometry shaders to the thread execution logic600 (FIG. 6) for processing. In some embodiments, thread dispatcher 604can also process runtime thread spawning requests from the executingshader programs.

In some embodiments, the execution units 608A-608N support aninstruction set that includes native support for many standard 3Dgraphics shader instructions, such that shader programs from graphicslibraries (e.g., Direct 3D and OpenGL) are executed with a minimaltranslation. The execution units support vertex and geometry processing(e.g., vertex programs, geometry programs, vertex shaders), pixelprocessing (e.g., pixel shaders, fragment shaders) and general-purposeprocessing (e.g., compute and media shaders). Each of the executionunits 608A-608N is capable of multi-issue single instruction multipledata (SIMD) execution and multi-threaded operation enables an efficientexecution environment in the face of higher latency memory accesses.Each hardware thread within each execution unit has a dedicatedhigh-bandwidth register file and associated independent thread-state.Execution is multi-issue per clock to pipelines capable of integer,single and double precision floating point operations, SIMD branchcapability, logical operations, transcendental operations, and othermiscellaneous operations. While waiting for data from memory or one ofthe shared functions, dependency logic within the execution units608A-608N causes a waiting thread to sleep until the requested data hasbeen returned. While the waiting thread is sleeping, hardware resourcesmay be devoted to processing other threads. For example, during a delayassociated with a vertex shader operation, an execution unit can performoperations for a pixel shader, fragment shader, or another type ofshader program, including a different vertex shader.

Each execution unit in execution units 608A-608N operates on arrays ofdata elements. The number of data elements is the “execution size,” orthe number of channels for the instruction. An execution channel is alogical unit of execution for data element access, masking, and flowcontrol within instructions. The number of channels may be independentof the number of physical Arithmetic Logic Units (ALUs) or FloatingPoint Units (FPUs) for a particular graphics processor. In someembodiments, execution units 608A-608N support integer andfloating-point data types.

The execution unit instruction set includes SIMD instructions. Thevarious data elements can be stored as a packed data type in a registerand the execution unit will process the various elements based on thedata size of the elements. For example, when operating on a 256-bit widevector, the 256 bits of the vector are stored in a register and theexecution unit operates on the vector as four separate 64-bit packeddata elements (Quad-Word (QW) size data elements), eight separate 32-bitpacked data elements (Double Word (DW) size data elements), sixteenseparate 16-bit packed data elements (Word (W) size data elements), orthirty-two separate 8-bit data elements (byte (B) size data elements).However, different vector widths and register sizes are possible.

One or more internal instruction caches (e.g., 606) are included in thethread execution logic 600 to cache thread instructions for theexecution units. In some embodiments, one or more data caches (e.g.,612) are included to cache thread data during thread execution. In someembodiments, a sampler 610 is included to provide texture sampling for3D operations and media sampling for media operations. In someembodiments, sampler 610 includes specialized texture or media samplingfunctionality to process texture or media data during the samplingprocess before providing the sampled data to an execution unit.

During execution, the graphics and media pipelines send threadinitiation requests to thread execution logic 600 via thread spawningand dispatch logic. Once a group of geometric objects has been processedand rasterized into pixel data, pixel processor logic (e.g., pixelshader logic, fragment shader logic, etc.) within the shader processor602 is invoked to further compute output information and cause resultsto be written to output surfaces (e.g., color buffers, depth buffers,stencil buffers, etc.). In some embodiments, a pixel shader or fragmentshader calculates the values of the various vertex attributes that areto be interpolated across the rasterized object. In some embodiments,pixel processor logic within the shader processor 602 then executes anapplication programming interface (API)-supplied pixel or fragmentshader program. To execute the shader program, the shader processor 602dispatches threads to an execution unit (e.g., 608A) via threaddispatcher 604. In some embodiments, pixel shader 602 uses texturesampling logic in the sampler 610 to access texture data in texture mapsstored in memory. Arithmetic operations on the texture data and theinput geometry data compute pixel color data for each geometricfragment, or discards one or more pixels from further processing.

In some embodiments, the data port 614 provides a memory accessmechanism for the thread execution logic 600 output processed data tomemory for processing on a graphics processor output pipeline. In someembodiments, the data port 614 includes or couples to one or more cachememories (e.g., data cache 612) to cache data for memory access via thedata port.

FIG. 7 is a block diagram illustrating a graphics processor instructionformats 700 according to some embodiments. In one or more embodiment,the graphics processor execution units support an instruction set havinginstructions in multiple formats. The solid lined boxes illustrate thecomponents that are generally included in an execution unit instruction,while the dashed lines include components that are optional or that areonly included in a sub-set of the instructions. In some embodiments,instruction format 700 described and illustrated are macro-instructions,in that they are instructions supplied to the execution unit, as opposedto micro-operations resulting from instruction decode once theinstruction is processed.

In some embodiments, the graphics processor execution units nativelysupport instructions in a 128-bit instruction format 710. A 64-bitcompacted instruction format 730 is available for some instructionsbased on the selected instruction, instruction options, and number ofoperands. The native 128-bit instruction format 710 provides access toall instruction options, while some options and operations arerestricted in the 64-bit instruction format 730. The native instructionsavailable in the 64-bit instruction format 730 vary by embodiment. Insome embodiments, the instruction is compacted in part using a set ofindex values in an index field 713. The execution unit hardwarereferences a set of compaction tables based on the index values and usesthe compaction table outputs to reconstruct a native instruction in the128-bit instruction format 710.

For each format, instruction opcode 712 defines the operation that theexecution unit is to perform. The execution units execute eachinstruction in parallel across the multiple data elements of eachoperand. For example, in response to an add instruction the executionunit performs a simultaneous add operation across each color channelrepresenting a texture element or picture element. By default, theexecution unit performs each instruction across all data channels of theoperands. In some embodiments, instruction control field 714 enablescontrol over certain execution options, such as channels selection(e.g., predication) and data channel order (e.g., swizzle). Forinstructions in the 128-bit instruction format 710 an exec-size field716 limits the number of data channels that will be executed inparallel. In some embodiments, exec-size field 716 is not available foruse in the 64-bit compact instruction format 730.

Some execution unit instructions have up to three operands including twosource operands, src0 720, src1 722, and one destination 718. In someembodiments, the execution units support dual destination instructions,where one of the destinations is implied. Data manipulation instructionscan have a third source operand (e.g., SRC2 724), where the instructionopcode 712 determines the number of source operands. An instruction'slast source operand can be an immediate (e.g., hard-coded) value passedwith the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726 specifying, for example, whether directregister addressing mode or indirect register addressing mode is used.When direct register addressing mode is used, the register address ofone or more operands is directly provided by bits in the instruction.

In some embodiments, the 128-bit instruction format 710 includes anaccess/address mode field 726, which specifies an address mode and/or anaccess mode for the instruction. In one embodiment the access mode isused to define a data access alignment for the instruction. Someembodiments support access modes including a 16-byte aligned access modeand a 1-byte aligned access mode, where the byte alignment of the accessmode determines the access alignment of the instruction operands. Forexample, when in a first mode, the instruction may use byte-alignedaddressing for source and destination operands and when in a secondmode, the instruction may use 16-byte-aligned addressing for all sourceand destination operands.

In one embodiment, the address mode portion of the access/address modefield 726 determines whether the instruction is to use direct orindirect addressing. When direct register addressing mode is used bitsin the instruction directly provide the register address of one or moreoperands. When indirect register addressing mode is used, the registeraddress of one or more operands may be computed based on an addressregister value and an address immediate field in the instruction.

In some embodiments instructions are grouped based on opcode 712bit-fields to simplify Opcode decode 740. For an 8-bit opcode, bits 4,5, and 6 allow the execution unit to determine the type of opcode. Theprecise opcode grouping shown is merely an example. In some embodiments,a move and logic opcode group 742 includes data movement and logicinstructions (e.g., move (mov), compare (cmp)). In some embodiments,move and logic group 742 shares the five most significant bits (MSB),where move (mov) instructions are in the form of 0000xxxxb and logicinstructions are in the form of 0001xxxxb. A flow control instructiongroup 744 (e.g., call, jump (jmp)) includes instructions in the form of0010xxxxb (e.g., 0x20). A miscellaneous instruction group 746 includes amix of instructions, including synchronization instructions (e.g., wait,send) in the form of 0011xxxxb (e.g., 0x30). A parallel math instructiongroup 748 includes component-wise arithmetic instructions (e.g., add,multiply (mul)) in the form of 0100xxxxb (e.g., 0x40). The parallel mathgroup 748 performs the arithmetic operations in parallel across datachannels. The vector math group 750 includes arithmetic instructions(e.g., dp4) in the form of 0101xxxxb (e.g., 0x50). The vector math groupperforms arithmetic such as dot product calculations on vector operands.

Graphics Pipeline

FIG. 8 is a block diagram of another embodiment of a graphics processor800. Elements of FIG. 8 having the same reference numbers (or names) asthe elements of any other figure herein can operate or function in anymanner similar to that described elsewhere herein, but are not limitedto such.

In some embodiments, graphics processor 800 includes a graphics pipeline820, a media pipeline 830, a display engine 840, thread execution logic850, and a render output pipeline 870. In some embodiments, graphicsprocessor 800 is a graphics processor within a multi-core processingsystem that includes one or more general purpose processing cores. Thegraphics processor is controlled by register writes to one or morecontrol registers (not shown) or via commands issued to graphicsprocessor 800 via a ring interconnect 802. In some embodiments, ringinterconnect 802 couples graphics processor 800 to other processingcomponents, such as other graphics processors or general-purposeprocessors. Commands from ring interconnect 802 are interpreted by acommand streamer 803, which supplies instructions to individualcomponents of graphics pipeline 820 or media pipeline 830.

In some embodiments, command streamer 803 directs the operation of avertex fetcher 805 that reads vertex data from memory and executesvertex-processing commands provided by command streamer 803. In someembodiments, vertex fetcher 805 provides vertex data to a vertex shader807, which performs coordinate space transformation and lightingoperations to each vertex. In some embodiments, vertex fetcher 805 andvertex shader 807 execute vertex-processing instructions by dispatchingexecution threads to execution units 852A-852B via a thread dispatcher831.

In some embodiments, execution units 852A-852B are an array of vectorprocessors having an instruction set for performing graphics and mediaoperations. In some embodiments, execution units 852A-852B have anattached L1 cache 851 that is specific for each array or shared betweenthe arrays. The cache can be configured as a data cache, an instructioncache, or a single cache that is partitioned to contain data andinstructions in different partitions.

In some embodiments, graphics pipeline 820 includes tessellationcomponents to perform hardware-accelerated tessellation of 3D objects.In some embodiments, a programmable hull shader 811 configures thetessellation operations. A programmable domain shader 817 providesback-end evaluation of tessellation output. A tessellator 813 operatesat the direction of hull shader 811 and contains special purpose logicto generate a set of detailed geometric objects based on a coarsegeometric model that is provided as input to graphics pipeline 820. Insome embodiments, if tessellation is not used, tessellation components(e.g., hull shader 811, tessellator 813, and domain shader 817) can bebypassed.

In some embodiments, complete geometric objects can be processed by ageometry shader 819 via one or more threads dispatched to executionunits 852A-852B, or can proceed directly to the clipper 829. In someembodiments, the geometry shader operates on entire geometric objects,rather than vertices or patches of vertices as in previous stages of thegraphics pipeline. If the tessellation is disabled the geometry shader819 receives input from the vertex shader 807. In some embodiments,geometry shader 819 is programmable by a geometry shader program toperform geometry tessellation if the tessellation units are disabled.

Before rasterization, a clipper 829 processes vertex data. The clipper829 may be a fixed function clipper or a programmable clipper havingclipping and geometry shader functions. In some embodiments, arasterizer and depth test component 873 in the render output pipeline870 dispatches pixel shaders to convert the geometric objects into theirper pixel representations. In some embodiments, pixel shader logic isincluded in thread execution logic 850. In some embodiments, anapplication can bypass the rasterizer and depth test component 873 andaccess un-rasterized vertex data via a stream out unit 823.

The graphics processor 800 has an interconnect bus, interconnect fabric,or some other interconnect mechanism that allows data and messagepassing amongst the major components of the processor. In someembodiments, execution units 852A-852B and associated cache(s) 851,texture and media sampler 854, and texture/sampler cache 858interconnect via a data port 856 to perform memory access andcommunicate with render output pipeline components of the processor. Insome embodiments, sampler 854, caches 851, 858 and execution units852A-852B each have separate memory access paths.

In some embodiments, render output pipeline 870 contains a rasterizerand depth test component 873 that converts vertex-based objects into anassociated pixel-based representation. In some embodiments, therasterizer logic includes a windower/masker unit to perform fixedfunction triangle and line rasterization. An associated render cache 878and depth cache 879 are also available in some embodiments. A pixeloperations component 877 performs pixel-based operations on the data,though in some instances, pixel operations associated with 2D operations(e.g. bit block image transfers with blending) are performed by the 2Dengine 841, or substituted at display time by the display controller 843using overlay display planes. In some embodiments, a shared L3 cache 875is available to all graphics components, allowing the sharing of datawithout the use of main system memory.

In some embodiments, graphics processor media pipeline 830 includes amedia engine 837 and a video front end 834. In some embodiments, videofront end 834 receives pipeline commands from the command streamer 803.In some embodiments, media pipeline 830 includes a separate commandstreamer. In some embodiments, video front-end 834 processes mediacommands before sending the command to the media engine 837. In someembodiments, media engine 837 includes thread spawning functionality tospawn threads for dispatch to thread execution logic 850 via threaddispatcher 831.

In some embodiments, graphics processor 800 includes a display engine840. In some embodiments, display engine 840 is external to processor800 and couples with the graphics processor via the ring interconnect802, or some other interconnect bus or fabric. In some embodiments,display engine 840 includes a 2D engine 841 and a display controller843. In some embodiments, display engine 840 contains special purposelogic capable of operating independently of the 3D pipeline. In someembodiments, display controller 843 couples with a display device (notshown), which may be a system integrated display device, as in a laptopcomputer, or an external display device attached via a display deviceconnector.

In some embodiments, graphics pipeline 820 and media pipeline 830 areconfigurable to perform operations based on multiple graphics and mediaprogramming interfaces and are not specific to any one applicationprogramming interface (API). In some embodiments, driver software forthe graphics processor translates API calls that are specific to aparticular graphics or media library into commands that can be processedby the graphics processor. In some embodiments, support is provided forthe Open Graphics Library (OpenGL), Open Computing Language (OpenCL),and/or Vulkan graphics and compute API, all from the Khronos Group. Insome embodiments, support may also be provided for the Direct3D libraryfrom the Microsoft Corporation. In some embodiments, a combination ofthese libraries may be supported. Support may also be provided for theOpen Source Computer Vision Library (OpenCV). A future API with acompatible 3D pipeline would also be supported if a mapping can be madefrom the pipeline of the future API to the pipeline of the graphicsprocessor.

Graphics Pipeline Programming

FIG. 9A is a block diagram illustrating a graphics processor commandformat 900 according to some embodiments. FIG. 9B is a block diagramillustrating a graphics processor command sequence 910 according to anembodiment. The solid lined boxes in FIG. 9A illustrate the componentsthat are generally included in a graphics command while the dashed linesinclude components that are optional or that are only included in asub-set of the graphics commands. The exemplary graphics processorcommand format 900 of FIG. 9A includes data fields to identify a targetclient 902 of the command, a command operation code (opcode) 904, andthe relevant data 906 for the command. A sub-opcode 905 and a commandsize 908 are also included in some commands.

In some embodiments, client 902 specifies the client unit of thegraphics device that processes the command data. In some embodiments, agraphics processor command parser examines the client field of eachcommand to condition the further processing of the command and route thecommand data to the appropriate client unit. In some embodiments, thegraphics processor client units include a memory interface unit, arender unit, a 2D unit, a 3D unit, and a media unit. Each client unithas a corresponding processing pipeline that processes the commands.Once the command is received by the client unit, the client unit readsthe opcode 904 and, if present, sub-opcode 905 to determine theoperation to perform. The client unit performs the command usinginformation in data field 906. For some commands an explicit commandsize 908 is expected to specify the size of the command. In someembodiments, the command parser automatically determines the size of atleast some of the commands based on the command opcode. In someembodiments commands are aligned via multiples of a double word.

The flow diagram in FIG. 9B shows an exemplary graphics processorcommand sequence 910. In some embodiments, software or firmware of adata processing system that features an embodiment of a graphicsprocessor uses a version of the command sequence shown to set up,execute, and terminate a set of graphics operations. A sample commandsequence is shown and described for purposes of example only asembodiments are not limited to these specific commands or to thiscommand sequence. Moreover, the commands may be issued as batch ofcommands in a command sequence, such that the graphics processor willprocess the sequence of commands in at least partially concurrence.

In some embodiments, the graphics processor command sequence 910 maybegin with a pipeline flush command 912 to cause any active graphicspipeline to complete the currently pending commands for the pipeline. Insome embodiments, the 3D pipeline 922 and the media pipeline 924 do notoperate concurrently. The pipeline flush is performed to cause theactive graphics pipeline to complete any pending commands. In responseto a pipeline flush, the command parser for the graphics processor willpause command processing until the active drawing engines completepending operations and the relevant read caches are invalidated.Optionally, any data in the render cache that is marked ‘dirty’ can beflushed to memory. In some embodiments, pipeline flush command 912 canbe used for pipeline synchronization or before placing the graphicsprocessor into a low power state.

In some embodiments, a pipeline select command 913 is used when acommand sequence requires the graphics processor to explicitly switchbetween pipelines. In some embodiments, a pipeline select command 913 isrequired only once within an execution context before issuing pipelinecommands unless the context is to issue commands for both pipelines. Insome embodiments, a pipeline flush command 912 is required immediatelybefore a pipeline switch via the pipeline select command 913.

In some embodiments, a pipeline control command 914 configures agraphics pipeline for operation and is used to program the 3D pipeline922 and the media pipeline 924. In some embodiments, pipeline controlcommand 914 configures the pipeline state for the active pipeline. Inone embodiment, the pipeline control command 914 is used for pipelinesynchronization and to clear data from one or more cache memories withinthe active pipeline before processing a batch of commands.

In some embodiments, commands for the return buffer state 916 are usedto configure a set of return buffers for the respective pipelines towrite data. Some pipeline operations require the allocation, selection,or configuration of one or more return buffers into which the operationswrite intermediate data during processing. In some embodiments, thegraphics processor also uses one or more return buffers to store outputdata and to perform cross thread communication. In some embodiments,configuring the return buffer state 916 includes selecting the size andnumber of return buffers to use for a set of pipeline operations.

The remaining commands in the command sequence differ based on theactive pipeline for operations. Based on a pipeline determination 920,the command sequence is tailored to the 3D pipeline 922 beginning withthe 3D pipeline state 930 or the media pipeline 924 beginning at themedia pipeline state 940.

The commands to configure the 3D pipeline state 930 include 3D statesetting commands for vertex buffer state, vertex element state, constantcolor state, depth buffer state, and other state variables that are tobe configured before 3D primitive commands are processed. The values ofthese commands are determined at least in part based on the particular3D API in use. In some embodiments, 3D pipeline state 930 commands arealso able to selectively disable or bypass certain pipeline elements ifthose elements will not be used.

In some embodiments, 3D primitive 932 command is used to submit 3Dprimitives to be processed by the 3D pipeline. Commands and associatedparameters that are passed to the graphics processor via the 3Dprimitive 932 command are forwarded to the vertex fetch function in thegraphics pipeline. The vertex fetch function uses the 3D primitive 932command data to generate vertex data structures. The vertex datastructures are stored in one or more return buffers. In someembodiments, 3D primitive 932 command is used to perform vertexoperations on 3D primitives via vertex shaders. To process vertexshaders, 3D pipeline 922 dispatches shader execution threads to graphicsprocessor execution units.

In some embodiments, 3D pipeline 922 is triggered via an execute 934command or event. In some embodiments, a register write triggers commandexecution. In some embodiments execution is triggered via a ‘go’ or‘kick’ command in the command sequence. In one embodiment, commandexecution is triggered using a pipeline synchronization command to flushthe command sequence through the graphics pipeline. The 3D pipeline willperform geometry processing for the 3D primitives. Once operations arecomplete, the resulting geometric objects are rasterized and the pixelengine colors the resulting pixels. Additional commands to control pixelshading and pixel back end operations may also be included for thoseoperations.

In some embodiments, the graphics processor command sequence 910 followsthe media pipeline 924 path when performing media operations. Ingeneral, the specific use and manner of programming for the mediapipeline 924 depends on the media or compute operations to be performed.Specific media decode operations may be offloaded to the media pipelineduring media decode. In some embodiments, the media pipeline can also bebypassed and media decode can be performed in whole or in part usingresources provided by one or more general purpose processing cores. Inone embodiment, the media pipeline also includes elements forgeneral-purpose graphics processor unit (GPGPU) operations, where thegraphics processor is used to perform SIMD vector operations usingcomputational shader programs that are not explicitly related to therendering of graphics primitives.

In some embodiments, media pipeline 924 is configured in a similarmanner as the 3D pipeline 922. A set of commands to configure the mediapipeline state 940 are dispatched or placed into a command queue beforethe media object commands 942. In some embodiments, commands for themedia pipeline state 940 include data to configure the media pipelineelements that will be used to process the media objects. This includesdata to configure the video decode and video encode logic within themedia pipeline, such as encode or decode format. In some embodiments,commands for the media pipeline state 940 also support the use of one ormore pointers to “indirect” state elements that contain a batch of statesettings.

In some embodiments, media object commands 942 supply pointers to mediaobjects for processing by the media pipeline. The media objects includememory buffers containing video data to be processed. In someembodiments, all media pipeline states must be valid before issuing amedia object command 942. Once the pipeline state is configured andmedia object commands 942 are queued, the media pipeline 924 istriggered via an execute command 944 or an equivalent execute event(e.g., register write). Output from media pipeline 924 may then be postprocessed by operations provided by the 3D pipeline 922 or the mediapipeline 924. In some embodiments, GPGPU operations are configured andexecuted in a similar manner as media operations.

Graphics Software Architecture

FIG. 10 illustrates exemplary graphics software architecture for a dataprocessing system 1000 according to some embodiments. In someembodiments, software architecture includes a 3D graphics application1010, an operating system 1020, and at least one processor 1030. In someembodiments, processor 1030 includes a graphics processor 1032 and oneor more general-purpose processor core(s) 1034. The graphics application1010 and operating system 1020 each execute in the system memory 1050 ofthe data processing system.

In some embodiments, 3D graphics application 1010 contains one or moreshader programs including shader instructions 1012. The shader languageinstructions may be in a high-level shader language, such as the HighLevel Shader Language (HLSL) or the OpenGL Shader Language (GLSL). Theapplication also includes executable instructions 1014 in a machinelanguage suitable for execution by the general-purpose processor core1034. The application also includes graphics objects 1016 defined byvertex data.

In some embodiments, operating system 1020 is a Microsoft® Windows®operating system from the Microsoft Corporation, a proprietary UNIX-likeoperating system, or an open source UNIX-like operating system using avariant of the Linux kernel. The operating system 1020 can support agraphics API 1022 such as the Direct3D API, the OpenGL API, or theVulkan API. When the Direct3D API is in use, the operating system 1020uses a front-end shader compiler 1024 to compile any shader instructions1012 in HLSL into a lower-level shader language. The compilation may bea just-in-time (JIT) compilation or the application can perform shaderpre-compilation. In some embodiments, high-level shaders are compiledinto low-level shaders during the compilation of the 3D graphicsapplication 1010. In some embodiments, the shader instructions 1012 areprovided in an intermediate form, such as a version of the StandardPortable Intermediate Representation (SPIR) used by the Vulkan API.

In some embodiments, user mode graphics driver 1026 contains a back-endshader compiler 1027 to convert the shader instructions 1012 into ahardware specific representation. When the OpenGL API is in use, shaderinstructions 1012 in the GLSL high-level language are passed to a usermode graphics driver 1026 for compilation. In some embodiments, usermode graphics driver 1026 uses operating system kernel mode functions1028 to communicate with a kernel mode graphics driver 1029. In someembodiments, kernel mode graphics driver 1029 communicates with graphicsprocessor 1032 to dispatch commands and instructions.

IP Core Implementations

One or more aspects of at least one embodiment may be implemented byrepresentative code stored on a machine-readable medium which representsand/or defines logic within an integrated circuit such as a processor.For example, the machine-readable medium may include instructions whichrepresent various logic within the processor. When read by a machine,the instructions may cause the machine to fabricate the logic to performthe techniques described herein. Such representations, known as “IPcores,” are reusable units of logic for an integrated circuit that maybe stored on a tangible, machine-readable medium as a hardware modelthat describes the structure of the integrated circuit. The hardwaremodel may be supplied to various customers or manufacturing facilities,which load the hardware model on fabrication machines that manufacturethe integrated circuit. The integrated circuit may be fabricated suchthat the circuit performs operations described in association with anyof the embodiments described herein.

FIG. 11 is a block diagram illustrating an IP core development system1100 that may be used to manufacture an integrated circuit to performoperations according to an embodiment. The IP core development system1100 may be used to generate modular, re-usable designs that can beincorporated into a larger design or used to construct an entireintegrated circuit (e.g., an SOC integrated circuit). A design facility1130 can generate a software simulation 1110 of an IP core design in ahigh level programming language (e.g., C/C++). The software simulation1110 can be used to design, test, and verify the behavior of the IP coreusing a simulation model 1112. The simulation model 1112 may includefunctional, behavioral, and/or timing simulations. A register transferlevel (RTL) design 1115 can then be created or synthesized from thesimulation model 1112. The RTL design 1115 is an abstraction of thebehavior of the integrated circuit that models the flow of digitalsignals between hardware registers, including the associated logicperformed using the modeled digital signals. In addition to an RTLdesign 1115, lower-level designs at the logic level or transistor levelmay also be created, designed, or synthesized. Thus, the particulardetails of the initial design and simulation may vary.

The RTL design 1115 or equivalent may be further synthesized by thedesign facility into a hardware model 1120, which may be in a hardwaredescription language (HDL), or some other representation of physicaldesign data. The HDL may be further simulated or tested to verify the IPcore design. The IP core design can be stored for delivery to a 3rdparty fabrication facility 1165 using non-volatile memory 1140 (e.g.,hard disk, flash memory, or any non-volatile storage medium).Alternatively, the IP core design may be transmitted (e.g., via theInternet) over a wired connection 1150 or wireless connection 1160. Thefabrication facility 1165 may then fabricate an integrated circuit thatis based at least in part on the IP core design. The fabricatedintegrated circuit can be configured to perform operations in accordancewith at least one embodiment described herein.

Exemplary System on a Chip Integrated Circuit

FIGS. 12-14 illustrate exemplary integrated circuits and associatedgraphics processors that may be fabricated using one or more IP cores,according to various embodiments described herein. In addition to whatis illustrated, other logic and circuits may be included, includingadditional graphics processors/cores, peripheral interface controllers,or general purpose processor cores.

FIG. 12 is a block diagram illustrating an exemplary system on a chipintegrated circuit 1200 that may be fabricated using one or more IPcores, according to an embodiment. Exemplary integrated circuit 1200includes one or more application processor(s) 1205 (e.g., CPUs), atleast one graphics processor 1210, and may additionally include an imageprocessor 1215 and/or a video processor 1220, any of which may be amodular IP core from the same or multiple different design facilities.Integrated circuit 1200 includes peripheral or bus logic including a USBcontroller 1225, UART controller 1230, an SPI/SDIO controller 1235, andan 125/12C controller 1240. Additionally, the integrated circuit caninclude a display device 1245 coupled to one or more of ahigh-definition multimedia interface (HDMI) controller 1250 and a mobileindustry processor interface (MIPI) display interface 1255. Storage maybe provided by a flash memory subsystem 1260 including flash memory anda flash memory controller. Memory interface may be provided via a memorycontroller 1265 for access to SDRAM or SRAM memory devices. Someintegrated circuits additionally include an embedded security engine1270.

FIG. 13 is a block diagram illustrating an exemplary graphics processor1310 of a system on a chip integrated circuit that may be fabricatedusing one or more IP cores, according to an embodiment. Graphicsprocessor 1310 can be a variant of the graphics processor 1210 of FIG.12. Graphics processor 1310 includes a vertex processor 1305 and one ormore fragment processor(s) 1315A1315N (e.g., 1315A, 13158, 1315C, 1315D,through 1315N-1, and 1315N). Graphics processor 1310 can executedifferent shader programs via separate logic, such that the vertexprocessor 1305 is optimized to execute operations for vertex shaderprograms, while the one or more fragment processor(s) 1315A-1315Nexecute fragment (e.g., pixel) shading operations for fragment or pixelshader programs. The vertex processor 1305 performs the vertexprocessing stage of the 3D graphics pipeline and generates primitivesand vertex data. The fragment processor(s) 1315A-1315N use the primitiveand vertex data generated by the vertex processor 1305 to produce aframebuffer that is displayed on a display device. In one embodiment,the fragment processor(s) 1315A-1315N are optimized to execute fragmentshader programs as provided for in the OpenGL API, which may be used toperform similar operations as a pixel shader program as provided for inthe Direct 3D API.

Graphics processor 1310 additionally includes one or more memorymanagement units (MMUs) 1320A-1320B, cache(s) 1325A-1325B, and circuitinterconnect(s) 1330A-1330B. The one or more MMU(s) 1320A-1320B providefor virtual to physical address mapping for graphics processor 1310,including for the vertex processor 1305 and/or fragment processor(s)1315A-1315N, which may reference vertex or image/texture data stored inmemory, in addition to vertex or image/texture data stored in the one ormore cache(s) 1325A-1325B. In one embodiment the one or more MMU(s)1320A-1320B may be synchronized with other MMUs within the system,including one or more MMUs associated with the one or more applicationprocessor(s) 1205, image processor 1215, and/or video processor 1220 ofFIG. 12, such that each processor 1205-1220 can participate in a sharedor unified virtual memory system. The one or more circuitinterconnect(s) 1330A-1330B enable graphics processor 1310 to interfacewith other IP cores within the SoC, either via an internal bus of theSoC or via a direct connection, according to embodiments.

FIG. 14 is a block diagram illustrating an additional exemplary graphicsprocessor 1410 of a system on a chip integrated circuit that may befabricated using one or more IP cores, according to an embodiment.Graphics processor 1410 can be a variant of the graphics processor 1210of FIG. 12. Graphics processor 1410 includes the one or more MMU(s)1320A-1320B, cache(s) 1325A-1325B, and circuit interconnect(s)1330A-1330B of the integrated circuit 1300 of FIG. 13.

Graphics processor 1410 includes one or more shader core(s) 1415A-1415N(e.g., 1415A, 1415B, 1415C, 1415D, 1415E, 1415F, through 1315N-1, and1315N), which provides for a unified shader core architecture in which asingle core or type or core can execute all types of programmable shadercode, including shader program code to implement vertex shaders,fragment shaders, and/or compute shaders. The exact number of shadercores present can vary among embodiments and implementations.Additionally, graphics processor 1410 includes an inter-core taskmanager 1405, which acts as a thread dispatcher to dispatch executionthreads to one or more shader core(s) 1415A-1415N and a tiling unit 1418to accelerate tiling operations for tile-based rendering, in whichrendering operations for a scene are subdivided in image space, forexample to exploit local spatial coherence within a scene or to optimizeuse of internal caches.

Exemplary Graphics Virtualization Architectures

Some embodiments of the invention are implemented on a platformutilizing full graphics processor unit (GPU) virtualization. As such, anoverview of the GPU virtualization techniques employed in one embodimentof the invention is provided below, followed by a detailed descriptionof an apparatus and method for pattern-driven page table shadowing.

One embodiment of the invention employs a full GPU virtualizationenvironment running a native graphics driver in the guest, and mediatedpass-through that achieves both good performance, scalability, andsecure isolation among guests. This embodiment presents a virtualfull-fledged GPU to each virtual machine (VM) which can directly accessperformance-critical resources without intervention from the hypervisorin most cases, while privileged operations from the guest aretrap-and-emulated at minimal cost. In one embodiment, a virtual GPU(vGPU), with full GPU features, is presented to each VM. VMs candirectly access performance-critical resources, without interventionfrom the hypervisor in most cases, while privileged operations from theguest are trap-and-emulated to provide secure isolation among VMs. ThevGPU context is switched per quantum, to share the physical GPU amongmultiple VMs.

FIG. 15 illustrates a high level system architecture on whichembodiments of the invention may be implemented which includes agraphics processing unit (GPU) 1500, a central processing unit (CPU)1520, and a system memory 1510 shared between the GPU 1500 and the CPU1520. A render engine 1502 fetches GPU commands from a command buffer1512 in system memory 1510, to accelerate graphics rendering usingvarious different features. The display engine 1504 fetches pixel datafrom the frame buffer 1514 and then sends the pixel data to externalmonitors for display.

Certain architectures use system memory 1510 as graphics memory, whileother GPUs may use on-die memory. System memory 1510 may be mapped intomultiple virtual address spaces by GPU page tables 1506. A 2 GB globalvirtual address space, called global graphics memory, accessible fromboth the GPU 1500 and CPU 1520, is mapped through global page tables.Local graphics memory spaces are supported in the form of multiple 2 GBlocal virtual address spaces, but are only limited to access from therender engine 1502, through local page tables. Global graphics memory ismostly the frame buffer 1514, but also serves as the command buffer1512. Large data accesses are made to local graphics memory whenhardware acceleration is in progress. Similar page table mechanisms areemployed by GPUs with on-die memory.

In one embodiment, the CPU 1520 programs the GPU 1500 throughGPU-specific commands, shown in FIG. 15, in a producer-consumer model.The graphics driver programs GPU commands into the command buffer 1512,including a primary buffer and a batch buffer, according to high levelprogramming APIs like OpenGL and DirectX. The GPU 1500 then fetches andexecutes the commands. The primary buffer, a ring buffer, may chainother batch buffers together. The terms “primary buffer” and “ringbuffer” are used interchangeably hereafter. The batch buffer is used toconvey the majority of the commands (up to ˜98%) per programming model.A register tuple (head, tail) is used to control the ring buffer. In oneembodiment, the CPU 1520 submits the commands to the GPU 1500 byupdating the tail, while the GPU 1500 fetches commands from head, andthen notifies the CPU 1520 by updating the head, after the commands havefinished execution.

As mentioned, one embodiment of the invention is implemented in a fullGPU virtualization platform with mediated pass-through. As such, everyVM is presented with a full-fledged GPU to run a native graphics driverinside a VM. The challenge, however, is significant in three ways: (1)complexity in virtualizing an entire sophisticated modern GPU, (2)performance due to multiple VMs sharing the GPU, and (3) secureisolation among the VMs without any compromise.

FIG. 16 illustrates a GPU virtualization architecture in accordance withone embodiment of the invention which includes a hypervisor 1610 runningon a GPU 1600, a privileged virtual machine (VM) 1620 and one or moreuser VMs 1631-1632. A virtualization stub module 1611 running in thehypervisor 1610 extends memory management to include extended pagetables (EPT) 1614 for the user VMs 1631-1632 and a privileged virtualmemory management unit (PVMMU) 1612 for the privileged VM 1620, toimplement the policies of trap and pass-through. In one embodiment, eachVM 1620, 1631-1632 runs the native graphics driver 1628 which candirectly access the performance-critical resources of the frame bufferand the command buffer, with resource partitioning as described below.To protect privileged resources, that is, the I/O registers and PTEs,corresponding accesses from the graphics drivers 1628 in user VMs1631-1632 and the privileged VM 1620, are trapped and forwarded to thevirtualization mediator 1622 in the privileged VM 1620 for emulation. Inone embodiment, the virtualization mediator 1622 uses hypercalls toaccess the physical GPU 1600 as illustrated.

In addition, in one embodiment, the virtualization mediator 1622implements a GPU scheduler 1626, which runs concurrently with the CPUscheduler 1616 in the hypervisor 1610, to share the physical GPU 1600among the VMs 1631-1632. One embodiment uses the physical GPU 1600 todirectly execute all the commands submitted from a VM, so it avoids thecomplexity of emulating the render engine, which is the most complexpart within the GPU. In the meantime, the resource pass-through of boththe frame buffer and command buffer minimizes the hypervisor's 1610intervention on CPU accesses, while the GPU scheduler 1626 guaranteesevery VM a quantum for direct GPU execution. Consequently, theillustrated embodiment achieves good performance when sharing the GPUamong multiple VMs.

In one embodiment, the virtualization stub 1611 selectively traps orpasses-through guest access of certain GPU resources. The virtualizationstub 1611 manipulates the EPT 1614 entries to selectively present orhide a specific address range to user VMs 1631-1632, while uses areserved bit of PTEs in the PVMMU 1612 for the privileged VM 1620, toselectively trap or pass-through guest accesses to a specific addressrange. In both cases, the peripheral input/output (PIO) accesses aretrapped. All the trapped accesses are forwarded to the virtualizationmediator 1622 for emulation while the virtualization mediator 1611 useshypercalls to access the physical GPU 1600.

As mentioned, in one embodiment, the virtualization mediator 1622emulates virtual GPUs (vGPUs) 1624 for privileged resource accesses, andconducts context switches amongst the vGPUs 1624. In the meantime, theprivileged VM 1620 graphics driver 1628 is used to initialize thephysical device and to manage power. One embodiment takes a flexiblerelease model, by implementing the virtualization mediator 1622 as akernel module in the privileged VM 1620, to ease the binding between thevirtualization mediator 1622 and the hypervisor 1610.

A split CPU/GPU scheduling mechanism is implemented via the CPUscheduler 1616 and GPU scheduler 1626. This is done because of the costof a GPU context switch may be over 1000 times the cost of a CPU contextswitch (e.g., ˜700 us vs. ˜300 ns). In addition, the number of the CPUcores likely differs from the number of the GPU cores in a computersystem. Consequently, in one embodiment, a GPU scheduler 1626 isimplemented separately from the existing CPU scheduler 1616. The splitscheduling mechanism leads to the requirement of concurrent accesses tothe resources from both the CPU and the GPU. For example, while the CPUis accessing the graphics memory of VM1 1631, the GPU may be accessingthe graphics memory of VM2 1632, concurrently.

As discussed above, in one embodiment, a native graphics driver 1628 isexecuted inside each VM 1620, 1631-1632, which directly accesses aportion of the performance-critical resources, with privilegedoperations emulated by the virtualization mediator 1622. The splitscheduling mechanism leads to the resource partitioning design describedbelow. To support resource partitioning better, one embodiment reservesa Memory-Mapped I/O (MMIO) register window to convey the resourcepartitioning information to the VM.

In one embodiment, the location and definition of virt_info has beenpushed to the hardware specification as a virtualization extension sothe graphics driver 1628 handles the extension natively, and future GPUgenerations follow the specification for backward compatibility.

While illustrated as a separate component in FIG. 16, in one embodiment,the privileged VM 1620 including the virtualization mediator 1622 (andits vGPU instances 1624 and GPU scheduler 1626) is implemented as amodule within the hypervisor 1610.

In one embodiment, the virtualization mediator 1622 manages vGPUs 1624of all VMs, by trap-and-emulating the privileged operations. Thevirtualization mediator 1622 handles the physical GPU interrupts, andmay generate virtual interrupts to the designated VMs 1631-1632. Forexample, a physical completion interrupt of command execution maytrigger a virtual completion interrupt, delivered to the renderingowner. The idea of emulating a vGPU instance per semantics is simple;however, the implementation involves a large engineering effort and adeep understanding of the GPU 1600. For example, approximately 700 I/Oregisters may be accessed by certain graphics drivers.

In one embodiment, the GPU scheduler 1626 implements a coarse-grainquality of service (QoS) policy. A particular time quantum may beselected as a time slice for each VM 1631-1632 to share the GPU 1600resources. For example, in one embodiment, a time quantum of 16 ms isselected as the scheduling time slice, because this value results in alow human perceptibility to image changes. Such a relatively largequantum is also selected because the cost of the GPU context switch isover 1000× that of the CPU context switch, so it can't be as small asthe time slice in the CPU scheduler 1616. The commands from a VM1631-1632 are submitted to the GPU 1600 continuously, until the guest/VMruns out of its time-slice. In one embodiment, the GPU scheduler 1626waits for the guest ring buffer to become idle before switching, becausemost GPUs today are non-preemptive, which may impact fairness. Tominimize the wait overhead, a coarse-grain flow control mechanism may beimplemented, by tracking the command submission to guarantee the piledcommands, at any time, are within a certain limit. Therefore, the timedrift between the allocated time slice and the execution time isrelatively small, compared to the large quantum, so a coarse-grain QoSpolicy is achieved.

In one embodiment, on a render context switch, the internal pipelinestate and I/O register states are saved and restored, and a cache/TLBflush is performed, when switching the render engine among vGPUs 1624.The internal pipeline state is invisible to the CPU, but can be savedand restored through GPU commands. Saving/restoring I/O register statescan be achieved through reads/writes to a list of the registers in therender context. Internal caches and Translation Lookaside Buffers (TLB)included in modern GPUs to accelerate data accesses and addresstranslations, must be flushed using commands at the render contextswitch, to guarantee isolation and correctness. The steps used to switcha context in one embodiment are: 1) save current I/O states, 2) flushthe current context, 3) use the additional commands to save the currentcontext, 4) use the additional commands to restore the new context, and5) restore I/O state of the new context.

As mentioned, one embodiment uses a dedicated ring buffer to carry theadditional GPU commands. The (audited) guest ring buffer may be reusedfor performance, but it is not safe to directly insert the commands intothe guest ring buffer, because the CPU may continue to queue morecommands, leading to overwritten content. To avoid a race condition, oneembodiment switches from the guest ring buffer to its own dedicated ringbuffer. At the end of the context switch, this embodiment switches fromthe dedicated ring buffer to the guest ring buffer of the new VM.

One embodiment reuses the privileged VM 1620 graphics driver toinitialize the display engine, and then manages the display engine toshow different VM frame buffers.

When two vGPUs 1624 have the same resolution, only the frame bufferlocations are switched. For different resolutions, the privileged VM mayuse a hardware scalar, a common feature in modern GPUs, to scale theresolution up and down automatically. Both techniques take meremilliseconds. In many cases, display management may not be needed suchas when the VM is not shown on the physical display (e.g., when it ishosted on the remote servers).

As illustrated in FIG. 16, one embodiment passes through the accesses tothe frame buffer and command buffer to accelerate performance-criticaloperations from a VM 1631-1632. For the global graphics memory space, 2GB in size, graphics memory resource partitioning and address spaceballooning techniques may be employed. For the local graphics memoryspaces, each also with a size of 2 GB, a per-VM local graphics memorymay be implemented through the render context switch, due to localgraphics memory being accessible only by the GPU 1600.

As mentioned, one embodiment partitions the global graphics memory amongVMs 1631-1632. As explained above, a split CPU/GPU scheduling mechanismrequires that the global graphics memory of different VMs can beaccessed simultaneously by the CPU and the GPU, so each VM must bepresented at any time with its own resources, leading to the resourcepartitioning approach for global graphics memory.

FIG. 17 illustrates additional details for one embodiment of a graphicsvirtualization architecture 1700 which includes multiple VMs, e.g., VM1730 and VM 1740, managed by hypervisor 1710, including access to a fullarray of GPU features in a GPU 1720. In various embodiments, hypervisor1710 may enable VM 1730 or VM 1740 to utilize graphics memory and otherGPU resources for GPU virtualization. One or more virtual GPUs (vGPUs),e.g., vGPUs 1760A and 1760B, may access the full functionality providedby GPU 1720 hardware based on the GPU virtualization technology. Invarious embodiments, hypervisor 1710 may track, manage resources andlifecycles of the vGPUs 1760A and 1760B as described herein.

In some embodiments, vGPUs 1760A-B may include virtual GPU devicespresented to VMs 1730, 1740 and may be used to interactive with nativeGPU drivers (e.g., as described above with respect to FIG. 16). VM 1730or VM 1740 may then access the full array of GPU features and usevirtual GPU devices in vGPUs 1760A-B to access virtual graphicsprocessors. For instance, once VM 1730 is trapped into hypervisor 1710,hypervisor 1710 may manipulate a vGPU instance, e.g., vGPU 1760A, anddetermine whether VM 1730 may access virtual GPU devices in vGPU 1760A.The vGPU context may be switched per quantum or event. In someembodiments, the context switch may happen per GPU render engine such as3D render engine 1722 or blitter render engine 1724. The periodicswitching allows multiple VMs to share a physical GPU in a manner thatis transparent to the workloads of the VMs.

GPU virtualization may take various forms. In some embodiments, VM 1730may be enabled with device pass-through, where the entire GPU 1720 ispresented to VM 1730 as if they are directly connected. Much like asingle central processing unit (CPU) core may be assigned for exclusiveuse by VM 1730, GPU 1720 may also be assigned for exclusive use by VM1730, e.g., even for a limited time. Another virtualization model istimesharing, where GPU 1720 or portions of it may be shared by multipleVMs, e.g., VM 1730 and VM 1740, in a fashion of multiplexing. Other GPUvirtualization models may also be used by apparatus 1700 in otherembodiments. In various embodiments, graphics memory associated with GPU1720 may be partitioned, and allotted to various vGPUs 1760A-B inhypervisor 1710.

In various embodiments, graphics translation tables (GTTs) may be usedby VMs or GPU 1720 to map graphics processor memory to system memory orto translate GPU virtual addresses to physical addresses. In someembodiments, hypervisor 1710 may manage graphics memory mapping viashadow GTTs, and the shadow GTTs may be held in a vGPU instance, e.g.,vGPU 1760A. In various embodiments, each VM may have a correspondingshadow GTT to hold the mapping between graphics memory addresses andphysical memory addresses, e.g., machine memory addresses undervirtualization environment. In some embodiments, the shadow GTT may beshared and maintain the mappings for multiple VMs. In some embodiments,each VM 1730 or VM 1740, may include both per-process and global GTTs.

In some embodiments, apparatus 1700 may use system memory as graphicsmemory. System memory may be mapped into multiple virtual address spacesby GPU page tables. Apparatus 1700 may support global graphics memoryspace and per-process graphics memory address space. The global graphicsmemory space may be a virtual address space, e.g., 2 GB, mapped througha global graphics translation table (GGTT). The lower portion of thisaddress space is sometimes called the aperture, accessible from both theGPU 1720 and CPU (not shown). The upper portion of this address space iscalled high graphics memory space or hidden graphics memory space, whichmay be used by GPU 1720 only. In various embodiments, shadow globalgraphics translation tables (SGGTTs) may be used by VM 1730, VM 1740,hypervisor 1710, or GPU 1720 for translating graphics memory addressesto respective system memory addresses based on a global memory addressspace.

In full GPU virtualization, a static global graphics memory spacepartitioning scheme may face a scalability problem. For example, for aglobal graphics memory space of 2 GB, the first 512 megabyte (MB)virtual address space may be reserved for aperture, and the rest ofthem, 1536 MB, may become the high (hidden) graphics memory space. Withthe static global graphics memory space partitioning scheme, each VMwith full GPU virtualization enabled may be allotted with 128 MBaperture and 384 MB high graphics memory space. Therefore, the 2 GBglobal graphics memory space may only accommodate a maximum of four VMs.

Besides the scalability problem, VMs with limited graphics memory spacemay also suffer performance degradation. Sometimes, severe performancedowngrade may be observed in some media-heavy workloads of a mediaapplication when it uses GPU media hardware acceleration extensively. Asan example, to decode one channel 1080p H.264/Advanced Video Coding(AVC) bit stream, at least 40 MB of graphics memory may be needed. Thus,for 10 channels of 1080p H264/AVC bit stream decoding, at least 400 MBof graphics memory space may be needed. Meanwhile, some graphic memoryspace may have to be set aside for surface composition/color conversion,switching display frame buffer during the decoding process, etc. In thiscase, 512 MB of graphics memory space per VM may be insufficient for aVM to run multiple video encoding or decoding.

In various embodiments, apparatus 100 may achieve GPU graphics memoryovercommitment with on-demand SGGTTs. In some embodiments, hypervisor1710 may construct SGGTTs on demand, which may include all theto-be-used translations for graphics memory virtual addresses fromdifferent GPU components' owner VMs.

In various embodiments, at least one VM managed by hypervisor 1710 maybe allotted with more than static partitioned global graphics memoryaddress space as well as memory. In some embodiments, at least one VMmanaged by hypervisor 1710 may be allotted with or able to access theentire high graphics memory address space. In some embodiments, at leastone VM managed by hypervisor 1710 may be allotted with or able to accessthe entire graphics memory address space.

Hypervisor/VMM 1710 may use command parser 1718 to detect the potentialmemory working set of a GPU rendering engine for the commands submittedby VM 1730 or VM 1740. In various embodiments, VM 1730 may haverespective command buffers (not shown) to hold commands from 3D workload1732 or media workload 1734. Similarly, VM 1740 may have respectivecommand buffers (not shown) to hold commands from 3D workload 1742 ormedia workload 1744. In other embodiments, VM 1730 or VM 1740 may haveother types of graphics workloads.

In various embodiments, command parser 1718 may scan a command from a VMand determine if the command contains memory operands. If yes, thecommand parser may read the related graphics memory space mappings,e.g., from a GTT for the VM, and then write it into a workload specificportion of the SGGTT. After the whole command buffer of a workload getsscanned, the SGGTT that holds memory address space mappings associatedwith this workload may be generated or updated. Additionally, byscanning the to-be-executed commands from VM 1730 or VM 1740, commandparser 1718 may also improve the security of GPU operations, such as bymitigating malicious operations.

In some embodiments, one SGGTT may be generated to hold translations forall workloads from all VMs. In some embodiments, one SGGTT may begenerated to hold translations for all workloads, e.g., from one VMonly. The workload specific SGGTT portion may be constructed on demandby command parser 1718 to hold the translations for a specific workload,e.g., 3D workload 1732 from VM 1730 or media workload 1744 from VM 1740.In some embodiments, command parser 1718 may insert the SGGTT into SGGTTqueue 1714 and insert the corresponding workload into workload queue1716.

In some embodiments, GPU scheduler 1712 may construct such on-demandSGGTT at the time of execution. A specific hardware engine may only usea small portion of the graphics memory address space allocated to VM1730 at the time of execution, and the GPU context switch happensinfrequently. To take advantage of such GPU features, hypervisor 1710may use the SGGTT for VM 1730 to only hold the in-execution andto-be-executed translations for various GPU components rather than theentire portion of the global graphics memory address space allotted toVM 1730.

GPU scheduler 1712 for GPU 1720 may be separated from the scheduler forCPU in apparatus 1700. To take the advantage of the hardware parallelismin some embodiments, GPU scheduler 1712 may schedule the workloadsseparately for different GPU engines, e.g., 3D render engine 1722,blitter render engine 1724, video command streamer (VCS) render engine1726, and video enhanced command streamer (VECS) render engine 1728. Forexample, VM 1730 may be 3D intensive, and 3D workload 1732 may need tobe scheduled to 3D render engine 1722 at a moment. Meanwhile, VM 1740may be media intensive, and media workload 1744 may need to be scheduledto VCS render engine 1726 and/or VECS render engine 1728. In this case,GPU scheduler 1712 may schedule 3D workload 1732 from VM 1730 and mediaworkload 1744 from VM 1740 separately.

In various embodiments, GPU scheduler 1712 may track in-executing SGGTTsused by respective render engines in GPU 1720. In this case, hypervisor1710 may retain a per-render engine SGGTT for tracking all in-executinggraphic memory working sets in respective render engines. In someembodiments, hypervisor 1710 may retain a single SGGTT for tracking allin-executing graphic memory working sets for all render engines. In someembodiments, such tracking may be based on a separate in-executing SGGTTqueue (not shown). In some embodiments, such tracking may be based onmarkings on SGGTT queue 1714, e.g., using a registry. In someembodiments, such tracking may be based on markings on workload queue1716, e.g., using a registry.

During the scheduling process, GPU scheduler 1712 may examine the SGGTTfrom SGGTT queue 1714 for a to-be-scheduled workload from workload queue1716. In some embodiments, to schedule the next VM for a particularrender engine, GPU scheduler 1712 may check whether the graphic memoryworking sets of the particular workload used by the VM for that renderengine conflict with the in-executing or to-be-executed graphic memoryworking sets by that render engine. In other embodiments, such conflictchecks may extend to check with the in-executing or to-be-executedgraphic memory working sets by all other render engines. In variousembodiments, such conflict checks may be based on the correspondingSGGTTs in SGGTT queue 1714 or based on SGGTTs retained by hypervisor1710 for tracking all in-executing graphic memory working sets inrespective render engines as discussed hereinbefore.

If there is no conflict, GPU scheduler 1712 may integrate thein-executing and to-be-executed graphic memory working sets together. Insome embodiments, a resulting SGGTT for the in-executing andto-be-executed graphic memory working sets for the particular renderengine may also be generated and stored, e.g., in SGGTT queue 1714 or inother data storage means. In some embodiments, a resulting SGGTT for thein-executing and to-be-executed graphic memory working sets for allrender engines associated with one VM may also be generated and storedif the graphics memory addresses of all these workloads do not conflictwith each other.

Before submitting a selected VM workload to GPU 1720, hypervisor 1710may write corresponding SGGTT pages into GPU 1720, e.g., to graphicstranslation tables 1750. Thus, hypervisor 1710 may enable this workloadto be executed with correct mappings in the global graphics memoryspace. In various embodiments, all such translation entries may bewritten into graphics translation tables 1750, either to lower memoryspace 1754 or upper memory space 1752. Graphics translation tables 1750may contain separate tables per VM to hold for these translation entriesin some embodiments. Graphics translation tables 1750 may also containseparate tables per render engine to hold for these translation entriesin other embodiments. In various embodiments, graphics translationtables 1750 may contain, at least, to-be-executed graphics memoryaddresses.

However, if there is a conflict determined by GPU scheduler 1712, GPUscheduler 1712 may then defer the schedule-in of that VM, and try toschedule-in another workload of the same or a different VM instead. Insome embodiments, such conflict may be detected if two or more VMs mayattempt to use a same graphics memory address, e.g., for a same renderengine or two different render engines. In some embodiments, GPUscheduler 1712 may change the scheduler policy to avoid selecting one ormore of the rendering engines, which have the potential to conflict witheach other. In some embodiments, GPU scheduler 1712 may suspend theexecution hardware engine to mitigate the conflict.

In some embodiments, memory overcommitment scheme in GPU virtualizationas discussed herein may co-exist with static global graphics memoryspace partitioning schemes. As an example, the aperture in lower memoryspace 1754 may still be used for static partition among all VMs. Thehigh graphics memory space in upper memory space 1752 may be used forthe memory overcommitment scheme. Compared to the static global graphicsmemory space partitioning scheme, memory overcommit scheme in GPUvirtualization may enable each VM to use the entire high graphics memoryspace in upper memory space 1752, which may allow some applicationsinside each VM to use greater graphic memory space for improvedperformance.

With static global graphics memory space partitioning schemes, a VMinitially claiming a large portion of memory may only use a smallportion at runtime, while other VMs may be in the status of shortage ofmemory. With memory overcommitment, a hypervisor may allocate memory forVMs on demand, and the saved memory may be used to support more VMs.With SGGTT based memory overcommitment, only graphic memory space usedby the to-be-executed workloads may be allocated at runtime, which savesgraphics memory space and supports more VMs to access GPU 1720.

Current architectures enable the hosting of GPU workloads in cloud anddata center environments. Full GPU virtualization is one of thefundamental enabling technologies used in the GPU Cloud. In full GPUvirtualization, the virtual machine monitor (VMM), particularly thevirtual GPU (vGPU) driver, traps and emulates the guest accesses toprivileged GPU resources for security and multiplexing, while passingthrough CPU accesses to performance critical resources, such as CPUaccess to graphics memory. GPU commands, once submitted, are directlyexecuted by the GPU without VMM intervention. As a result, close tonative performance is achieved.

Current systems use the system memory for GPU engines to access a GlobalGraphics Translation Table (GGTT) and/or a Per-Process GraphicsTranslation Table (PPGTT) to translate from GPU graphics memoryaddresses to system memory addresses. A shadowing mechanism may be usedfor the guest GPU page table's GGTT/PPGTT.

The VMM may use a shadow PPGTT which is synchronized to the guest PPGTT.The guest PPGTT is write-protected so that the shadow PPGTT can becontinually synchronized to the guest PPGTT by trapping and emulatingthe guest modifications of its PPGTT. Currently, the GGTT for each vGPUis shadowed and partitioned among each VM and the PPGTT is shadowed andper VM (e.g., on a per process basis). Shadowing for the GGTT page tableis straightforward since the GGTT PDE table stays in the PCI bar0 MMIOrange. However, the shadow for the PPGTT relies on write-protection ofthe Guest PPGTT page table and the traditional shadow page table is verycomplicated (and therefore buggy) and inefficient. For example, the CPUshadow page table has ˜30% performance overhead in currentarchitectures. Thus, in some of these systems an enlightened shadow pagetable is used, which modifies the guest graphics driver to cooperate inidentifying a page used for the page table page, and/or when it isreleased.

The embodiments of the invention include a memory management unit (MMU)such as an I/O memory management unit (IOMMU) to remap from a guestPPGTT-mapped GPN (guest page numbers) to HPN (host page number), withoutrelying on the low efficiency/complicated shadow PPGTT. At the sametime, one embodiment retains the global shadow GGTT page table foraddress ballooning. These techniques are referred to generally as hybridlayer of address mapping (HLAM).

An IOMMU by default cannot be used in certain mediated pass-througharchitectures since only a single second level translation is availablewith multiple VMs. One embodiment of the invention resolves thisproblem, utilizing the following techniques:

1. Using the IOMMU to conduct two layers of translation without theshadow PPGTT. In particular, in one embodiment, the GPU translates fromgraphics memory address (GM_ADDR) to GPN, and the IOMMU translates fromthe GPN to HPN, rather than the shadow PPGTT which translates from theGM_ADDR to HPN with write-protection applied to the guest PPGTT.

2. In one embodiment, the IOMMU page table is managed per VM, and isswitched (or maybe partially switched) when the vGPU is switched. Thatis, the corresponding VM's IOMMU page table is loaded when the VM/vGPUis scheduled in.

3. However, the GGTT-mapped addresses are shared in one embodiment, andthis global shadow GGTT must remain valid because the vCPU may accessthe GGTT-mapped address (e.g., such as the aperture), even when the vGPUof this VM is not scheduled in. As such, one embodiment of the inventionuses a hybrid layer of address translation which retains the globalshadow GGTT, but directly uses the guest PPGTT.

4. In one embodiment, the GPN address space is partitioned to shift theGGTT-mapped GPN address (which becomes input to the IOMMU, like the GPN)to a dedicated address range. This can be achieved by trapping andemulating the GGTT page table. In one embodiment, the GPN is modifiedfrom the GGTT with a large offset to avoid overlap with the PPGTT in theIOMMU mapping.

FIG. 18 illustrates an architecture employed in one embodiment in whichan IOMMU 1830 is enabled for device virtualization. The illustratedarchitecture includes two VMs 1801, 1811 executed on hypervisor/VMM 1820(although the underlying principles of the invention may be implementedwith any number of VMs). Each VM 1801, 1811 includes a driver 1802, 1812(e.g., a native graphics driver) which manages a guest PPGTT and GGTT1803, 1813, respectively. The illustrated IOMMU 1830 includes a HLAMmodule 1831 for implementing the hybrid layer of address mappingtechniques described herein. Notably, in this embodiment, shadow PPGTTsare not present.

In one embodiment, the entire Guest VM's (guest VM 1811 in the example)GPN to HPN translation page table 1833 is prepared in the IOMMU mapping,and each vGPU switch triggers an IOMMU page table swap. That is, as eachVM 1801, 1811 is scheduled in, its corresponding GPN to HPN translationtable 1833 is swapped in. In one embodiment, the HLAM 1831differentiates between GGTT GPNs and PPGTT GPNs and modifies the GGTTGPNs so that they do not overlap with the PPGTT GPNs when performing alookup in the translation table 1833. In particular, in one embodiment,virtual GPN generation logic 1832 converts the GGTT GPN into a virtualGPN which is then used to perform a lookup in the translation table 1833to identify the corresponding HPN.

In one embodiment, the virtual GPN is generated by shifting the GGTT bya specified (potentially large) offset to ensure that the mappedaddresses do not overlap/conflict with the PPGTT GPN. In addition, inone embodiment, since the CPU may access the GGTT mapped address (e.g.,the aperture) anytime, the global shadow GGTT will always be valid andremain in the per VM's IOMMU mapping 1833.

In one embodiment, the hybrid layer address mapping 1831 solutionpartitions the IOMMU address range into two parts: a lower part reservedfor PPGTT GPN-to-HPN translation, and an upper part reserved for GGTTvirtual GPN-to-HPN translation. Since the GPN is provided by theVM/Guest 1811, the GPN should be in the range of the guest memory size.In one embodiment, the guest PPGTT page tables are left unaltered andall GPNs from the PPGTT are directly send to the graphics translationhardware/IOMMU by the workload execution. However, in one embodiment,the MMIO read/write from guest VMs is trapped and GGTT page tablechanges are captured and altered as described herein (e.g., adding alarge offset to the GPN in order to ensure no overlap with the PPGTTmapping in the IOMMU).

Remote Virtualized Graphics Processing

In some embodiments of the invention, a server performs graphicsvirtualization, virtualizing physical GPUs and running graphicsapplications on behalf of clients. FIG. 19 illustrates one suchembodiment in which two clients 1901-1902 are connected to servers 1930over a network 1910 such as the Internet and/or a private network. Theservers 1930 implement a virtualized graphics environment in which ahypervisor 1960 allocates resources from one or more physical GPUs 1938,presenting the resources as virtual GPUs 1934-1935 to VMs/applications1932-1933. The graphics processing resources may allocated in accordancewith resource allocation policies 1961 which may cause the hypervisor1960 to allocate resources based on the requirements of the applications1932-1933 (e.g., higher performance graphics applications requiring moreresources), the user account associated with the applications 1932-1933(e.g., with certain users paying a premium for higher performance),and/or the current load on the system. The GPU resources being allocatedmay include, for example, sets of graphics processing engines such as 3Dengines, blit engines, execution units, and media engines, to name afew.

In one embodiment, a user of each client 1901-1902 has an account on theservice hosting the server(s) 1930. For example, the service may offer asubscription service to provide users remote access to onlineapplications 1932-1933 such as video games, productivity applications,and multi-player virtual reality applications. In one embodiment, theapplications are executed remotely on a virtual machine in response touser input 1907-1908 from the clients 1901-1902. Although notillustrated in FIG. 19, one or more CPUs may also be virtualized andused to execute the applications 1932-1933, with graphics processingoperations offloaded to the vGPUs 1934-1935.

In one embodiment, a sequence of image frames are generated by the vGPUs1934-1935 in response to the execution of the graphics operations. Forexample, in a first person shooter game, a user may specify input 1907to move a character around a fantasy world. In one embodiment, theresulting images are compressed (e.g., by compression circuitry/logic,not shown) and streamed over the network 1910 to the clients 1901-1902.In one implementation, a video compression algorithm such as H.261 maybe used; however, various different compression techniques may be used.Decoders 1905-1906 decode the incoming video streams, which are thenrendered on respective displays 1903-1904 of the clients 1901-1902.

Using the system illustrated in FIG. 19, high performance graphicsprocessing resources such as GPUs 1938 may be allocated to differentclients who subscribe to the service. In an online gamingimplementation, for example, the servers 1930 may host new video gamesas they are released. The video game program code is then executed inthe virtualized environment and the resulting video frames compressedand streamed to each client 1901-1902. The clients 1901-1902 in thisarchitecture do not require significant graphics processing resources.For example, even a relatively low power smartphone or tablet with adecoder 1905-1906 will be capable of decompressing a video stream. Thus,the latest graphics-intensive video games may be played on any type ofclient capable of compressing video. While video games are described asone possible implementation, the underlying principles of the inventionmay be used for any form of application which requires graphicsprocessing resources (e.g., graphic design applications, interactive andnon-interactive ray tracing applications, productivity software, videoediting software, etc).

Apparatus and Method for Implementing Different Views for Different VMS

In one embodiment, a driver and/or slice allocation hardware within theGPU allocates different sets of graphics processing resources (“slices”)to different virtual machines (VMs) based on variables such as theprocessing or latency requirements of the VMs and QoS guaranteesassociated with the VMs.

FIG. 20 illustrates one implementation in which different sets of slices2010-2017 of the underlying graphics processing hardware are allocatedto each of a plurality of VMs 2000-2003. In the illustrated example, VM02000 has been allocated 6 slices, and VMs 1-3 2001-2003 have beenallocated 2 slices each. In this example, the slices 2010-2017 maycomprise a homogeneous or heterogeneous set of graphics processingresources such as execution units (EUs), data ports (e.g., to acommunication fabric), shared local memory (SLM), samplers, pixel backend resources, 3D processing resources (e.g., fixed function units),media encode/decode resources, and rasterization resources, to name afew. The media encode/decode units 2020-2021 may be allocated separatelyfrom the slices 2010-2017 and/or may be integrated within one or more ofthe slices 2010-2017. In one embodiment, slice configuration logic 2018logically subdivides the graphics processing resources of the GPU 2010into the plurality of slices 2010-1017. For example, the sliceconfiguration logic 2018 may include sets of programmable registers toassociate each resource with a particular slice (e.g., using a slicenumber or other ID code to identify each slice).

The allocation of the slices may be performed by a graphics driver 2005(or Hypervisor), and/or dedicated slice allocation hardware 2006. Theslice allocation hardware 2006, for example, may comprise a set ofregisters to store the mapping between the VMs 2000-2003 and slices2010-2014, context data for each of the VMs 2000-2003, circuitry forrouting instructions and data between the VMs 2000-2003 and slices2010-2017.

In one embodiment, slices may be allocated based on the requirements ofeach particular VM. For example, if an interactive, low latency videogame or application is running on VM0 2000, then the driver/allocationhardware 2005-2006 may allocate a relatively larger number of slices tothis VM compared with other VMs 2001-2003 that are runningnon-interactive and/or latency-tolerant applications. In the specificexample shown in FIG. 20, VM 2000 is allocated 6 slices while each ofthe other VMs 2001-2003 are allocated 2 slices.

One embodiment also provides support for per slice preemption. Forexample, if a particular slice needs to be reallocated from a first VMto a second VM, then the first VM's slice may be preempted on behalf ofthe second VM. In such a case, the slice allocation hardware 2006 maysave the context associated with the first VM may which may be laterrestored to the same slice or a different slice. In one embodiment, theslice allocations may change dynamically based on the workloads on eachof the VMs 2000-2003. For example, if VM1 2001 begins executing alatency-sensitive and/or processing-intensive application (e.g., a 3Dinteractive video game or virtual reality application), then one or moreslices from the other VMs may be dynamically reallocated to VM1 2001.

In addition, in one embodiment, each VM 2000-2003 may be assigned apriority upon which slice allocation and preemption is based. Forexample, VM0 2000 may be executed on behalf of a user who pays a premiumfor low latency and/or high performance. As such, VM0 2000 may beallocated a larger number of slices. Priorities also may be set based onthe type of app/VM. For example, if a particular VM requires very lowlatency (e.g., an interactive 3D game), then this VM may be assigned thehighest priority available. By contrast, if a VM is non-interactiveand/or latency-tolerant, then this VM may be assigned a relatively lowerpriority. In addition, priorities may be set based on whether an app isa foreground app or a background app (e.g., providing higher prioritiesto foreground apps).

In addition to assigning slices based on priority and/or processingrequirements, one embodiment allocates slices to VMs based on a qualityof service (QoS) associated with a VM. For example, certain customersmay pay a premium for a guaranteed level of performance. In oneembodiment, this level of performance is achieved by guaranteeing acertain number of slices for a particular VM assigned to thesecustomers.

A method in accordance with one embodiment of the invention isillustrated in FIG. 21. The method may be implemented within the contextof the graphics processing architectures described herein, but is notlimited to any particular architecture.

At 2101 an initial allocation of slices to VMs is performed based onpriorities and/or processing requirements of the VMs. For example,priorities may be set based on a guaranteed quality of service andprocessing requirements may be based on the type of applications beingrun on each VM. At 2102, the priorities and/or processing requirementsare monitored to detect changes. If changes are detected, at 2103, thena determination is made as to whether slices should be reallocatedbetween VMs and/or whether new slices should be made available (e.g.,changed from a low power/sleep state to an active state). If not, thenthe existing allocation is maintained at 2105. If so, then at 2106, oneor more slices are reallocated in accordance with the priorities and/orprocessing requirements.

Virtualized Performance Monitoring and Reporting

A virtualized performance counting sub-system of one embodiment performssignaling and counting based on virtual machine (VM) identity. Inparticular, virtualized counters may be used to monitor differentattributes associated with the VM such as instructions completed percycle (IPC), cache misses, memory accesses, and I/O bandwidthutilization, to name a few. A pool of virtual counters may be allocateddynamically based on VM monitoring requirements. The data collected bythe counters may then be used for reporting. Note that any type of GPUevents may be logged using the techniques described herein (thesetechniques are not limited to counters).

One embodiment of a virtualized performance counting sub-system includesvirtualized counters for signaling and counting based on VM identity. Anexemplary architecture is illustrated in FIG. 22, which shows aplurality of units A-D 2201-2203 interconnected over various links. Theunits may represent any type of unit within a processor such as aninstruction fetch unit, a decode unit, a scheduler unit, an executionunit, an individual functional unit within an execution unit, a cacheunit, a 3D graphics engine, an instruction dispatch unit, a shader, atraversal unit, a sampler, a media unit, a register allocation unit, amemory controller unit, or a retirement unit, to name a few. In oneembodiment, the interfaces on the units 2201-2203 and/or the internalsignals over the various buses/links are monitored by a plurality ofmonitors 2210. For example, the various signals may be monitored for thepurposes of performance checking.

In one embodiment, the monitors 2210 tap the internal signals and storemonitored results within a set of statistics counters 2205. Thestatistics counters 2205 may be dynamically programmed to collectstatistical data for each VM. For example, one statistics counter 2205may collect instructions retired per cycle (IPC) for VM₀ and another maycollect cache misses for VM₁. The programmable statistics counters 2205may accumulate data on a per-thread basis, per-virtual processor basis,per-core basis, and/or per-chip basis. Examples of accumulators includevirtual thread/instruction count; virtual and physical core cycles perinstruction, cache misses, TLB misses, and pipeline flushes;chip/coherence and data interconnect bus utilization and read-writerates.

In a virtualized embodiment, a hypervisor/VMM may allocate memorybuffers for collection of the statistics counter data 2210 for eachcore, chip, and virtual processor in the system. When a VM is executed,the hypervisor points the VM to one of the memory buffers associatedwith the VM to start the accumulation of the counters for the VM.

In one embodiment, a report generator 2220, which may be implemented inhardware, software or any combination thereof, reports the values ofthese statistics counters 2205 out to memory 2230 (e.g., DRAM systemmemory) where they may be accessed by software, either on demand orperiodically. As illustrated, the report generator 2220 may store areport for each VM in a region in memory 2230 associated with each VM.reports may be stored in a designated region in memory. In oneembodiment, this VM statistical counter data is made available toexternal monitoring tools through authorized OS and Hardware ManagementConsole interfaces. In one embodiment, while the reporting is done, eachset of counters report out to VM's allocated address space in the memory2230.

One embodiment performs this monitoring and reporting on a VM-awarebasis. This may be accomplished, for example, by attaching a VM ID toeach of the signals that are is being monitored (e.g., VM0, VM1, . . .VMn). In one embodiment, the signals comprise packets of data associatedwith each VM and a VM ID included in each of the packets to identify itsassociated VM. This information is passed on to the statistics counters2205 which are provisioned (apportioned) to the various VMs asillustrated. Statistics are then collected by each of these counters forthe appropriate VMs.

In one embodiment, multiple sets of special purpose registers may beincluded in the GPU or CPU to collect performance monitoring data foreach VM. The statistics counters 2205, for example, may include the setsof registers dynamically allocated to each VM.

A method in accordance with one embodiment of the invention isillustrated in FIG. 23. The method may be implemented within the contextof the graphics processing architectures described herein, but is notlimited to any particular architecture.

At 2301, a plurality of monitors are specified for monitoring VM-awaresignals within a GPU and/or CPU pipeline. At 2302, a plurality ofstatistics counters are programmed to record performance data based onthe VM-aware signals. For example, the performance data data may bestored in different sets of special purpose registers associated witheach VM. At 2303, VM-aware reports are generated by storing performancedata to regions in memory allocated to each VM.

Workload Synchronization Between VMS

Existing producer/consumer systems identify the producer/consumerarrangement up front by sharing a memory page. The producer writes tothe memory page and the consumer to see if the data is there and readsit. However, implementing shared memory pages can be difficult. Inaddition, current solutions which identify specific engines up front areinflexible, particularly given the fact that there are many differentengines today which exchange data.

One embodiment of the invention implements a producer/consumerinfrastructure to support intercommunication between graphics engines(e.g., 3D engines, blit engines, media engines, etc). In one embodiment,a graphics scheduler (e.g., the GuC) acts as a central coordinationpoint between producers and consumers, allowing a more flexible,scalable design. In one implementation, the scheduler maintains a set ofdouble-buffered coalescing registers for each virtual function (VF). Aproducer sends a 32b vector to the scheduler which includes a 5b VFnumber and a 27b producer token number. In one implementation, the 32bvector is coalesced with any current vectors stored in thedouble-buffered coalescing registers (e.g., being ORed together with thecurrent vector(s) as described below). Coalescing producer tokens inthis manner avoids buffer overflow which may occur in systems thatsimply queue incoming producer tokens using queues that have a limitednumber of entries. The scheduler interprets the current vector in thecoalescing register to map producers to consumers (e.g., mapping thosewith the same token numbers).

FIG. 24 illustrates one implementation in which a graphics scheduler2430 (e.g., the graphics microcontroller or “GuC” in someimplementations) of a GPU 2410 acts as a central coordination pointbetween producers 2425 and consumers 2426, allowing a more flexible,scalable design. By way of example, and not limitation, the illustratedproducer 2425 may be a 3D engine and the consumer 2426 may be a blitengine. However, any type of producer may be communicatively coupledwith any type of consumer using the techniques described herein.Moreover, although a single producer/consumer pair are illustrated inFIG. 24, the techniques described herein may be used to couple multipleproducers to multiple consumers.

In one implementation, the scheduler 2430 maintains a set ofdouble-buffered coalescing registers 2431-2432 for each virtual function(VF). As mentioned, a producer 2425 may send a 32b vector to thescheduler 2430 which includes a 5b virtual function number and a 27bproducer token number (although the underlying principles of theinvention are not limited to any particular vector size). In oneimplementation, the 32b vector is coalesced with any current vectorsstored in the double-buffered coalescing register. In particular, in oneimplementation a token coalescer 2435 combines the current value storedin a register 2431 with the value provided in the new 32 b message andstores the coalesced result back in the register 2431. For example, eachproducer may be identified by a different bit value within the vector.The token coalescer 2435 may OR the current value in the register 2431with the new vector provided by the producer 2425. Thus, if the newproducer token has bit 0 set (identifying producer #1), and the currentregister value has bit 0 set to 0, then the new value will set bit 0to 1. If bit 0 is already set at 1 in the register 2431, then it willremain set at 1. As another example, if the current value in theregister 2431 is bit 0=1, bit1=0, bit2=0, and the new vector identifiesproducer#2 (bit 1=1), then the new register value will be bit 0=1,bit1=1, bit 2=0). In this way, any number of vectors from differentproducers may be efficiently stored within the registers 2431-2433,without overloading a queue or buffer as in prior systems.

As illustrated, the kernel mode driver 2425 may add a producer tokennumber to the context for which the engine 2425 is being executed. Theconsumer 2426 may register with the graphics scheduler 2430 byidentifying the same token number to establish a producer/consumerrelationship with producer 2425.

FIG. 25 illustrates additional details of how the dual buffering isperformed in one embodiment. The dual-buffered register 2431 includes aregister front copy 2436 and a register back copy 2437. The engines 2425will keep sending updates with specific bits set to 1 (i.e., the tokens)and the values are coalesced in the register front copy 2436, wheremultiple engine updates are ORed together as described above. At somepoint, the scheduler 2430 decides to service these messages, so it willread the register front copy 2436. Once the scheduler 2430 reads theregister front copy 2436, it will reset these bits to indicate thatthese have been serviced. The reset of bits may be done, for example, bythe scheduler 2430 writing to clear the register front copy 2436.However, between the time that the scheduler 2430 reads the registerfront copy 2436 and clears the bits, more updates from engines 2425 mayarrive. These should not reach register front copy 2436 because thescheduler has already taken a snapshot of this register. So, in oneembodiment, the incoming engine updates are accumulated in register backcopy 2437. Once the scheduler clears the register front copy 2436, thenthe register back copy 2437 updates the register front copy 2436 withthe latest updates.

FIG. 26 illustrates a method in accordance with one embodiment of theinvention. The method may be performed using the architectures describedherein but is not limited to any particular graphics processingarchitecture or system.

At 2601, a producer token number is added to the context (e.g., by theKVM as described above). At 2601, the producer provides an N bit vectorcontaining the token number and virtual function number. The virtualfunction number may identify a particular virtual machine executed onthe system. If an existing vector is stored in the register, determinedat 2602, then the N bit vector is combined with the current vector togenerate a new vector, potentially identifying multiple producers. Thenew value is then stored in the register. At 2604, producers andconsumers are interconnected using the new vector (e.g., via thescheduler as discussed above). If an existing vector is not stored inthe register, then at 2605, the N bit vector is stored in the registerand producers/consumers are interconnected at 2604.

Coalescing producer tokens in this manner avoids buffer overflow whichmay occur in systems that simply queue incoming producer tokens withinqueues that have a limited number of entries. It also solves problemsassociated with sharing memory pages between multipleproducers/consumers.

In some embodiments, a graphics processing unit (GPU) is communicativelycoupled to host/processor cores to accelerate graphics operations,machine-learning operations, pattern analysis operations, and variousgeneral purpose GPU (GPGPU) functions. The GPU may be communicativelycoupled to the host processor/cores over a bus or another interconnect(e.g., a high-speed interconnect such as PCIe or NVLink). In otherembodiments, the GPU may be integrated on the same package or chip asthe cores and communicatively coupled to the cores over an internalprocessor bus/interconnect (i.e., internal to the package or chip).Regardless of the manner in which the GPU is connected, the processorcores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

In the following description, numerous specific details are set forth toprovide a more thorough understanding. However, it will be apparent toone of skill in the art that the embodiments described herein may bepracticed without one or more of these specific details. In otherinstances, well-known features have not been described to avoidobscuring the details of the present embodiments.

System Overview

FIG. 27 is a block diagram illustrating a computing system 2700configured to implement one or more aspects of the embodiments describedherein. The computing system 2700 includes a processing subsystem 2701having one or more processor(s) 2702 and a system memory 2704communicating via an interconnection path that may include a memory hub2705. The memory hub 2705 may be a separate component within a chipsetcomponent or may be integrated within the one or more processor(s) 2702.The memory hub 2705 couples with an I/O subsystem 2711 via acommunication link 2706. The I/O subsystem 2711 includes an I/O hub 2707that can enable the computing system 2700 to receive input from one ormore input device(s) 2708. Additionally, the I/O hub 2707 can enable adisplay controller, which may be included in the one or moreprocessor(s) 2702, to provide outputs to one or more display device(s)2710A. In one embodiment the one or more display device(s) 2710A coupledwith the I/O hub 2707 can include a local, internal, or embedded displaydevice.

In one embodiment the processing subsystem 2701 includes one or moreparallel processor(s) 2712 coupled to memory hub 2705 via a bus or othercommunication link 2713. The communication link 2713 may be one of anynumber of standards based communication link technologies or protocols,such as, but not limited to PCI Express, or may be a vendor specificcommunications interface or communications fabric. In one embodiment theone or more parallel processor(s) 2712 form a computationally focusedparallel or vector processing system that an include a large number ofprocessing cores and/or processing clusters, such as a many integratedcore (MIC) processor. In one embodiment the one or more parallelprocessor(s) 2712 form a graphics processing subsystem that can outputpixels to one of the one or more display device(s) 2710A coupled via theI/O Hub 2707. The one or more parallel processor(s) 2712 can alsoinclude a display controller and display interface (not shown) to enablea direct connection to one or more display device(s) 2710B.

Within the I/O subsystem 2711, a system storage unit 2714 can connect tothe I/O hub 2707 to provide a storage mechanism for the computing system2700. An I/O switch 2716 can be used to provide an interface mechanismto enable connections between the I/O hub 2707 and other components,such as a network adapter 2718 and/or wireless network adapter 2719 thatmay be integrated into the platform, and various other devices that canbe added via one or more add-in device(s) 2720. The network adapter 2718can be an Ethernet adapter or another wired network adapter. Thewireless network adapter 2719 can include one or more of a Wi-Fi,Bluetooth, near field communication (NFC), or other network device thatincludes one or more wireless radios.

The computing system 2700 can include other components not explicitlyshown, including USB or other port connections, optical storage drives,video capture devices, and the like, may also be connected to the I/Ohub 2707. Communication paths interconnecting the various components inFIG. 27 may be implemented using any suitable protocols, such as PCI(Peripheral Component Interconnect) based protocols (e.g., PCI-Express),or any other bus or point-to-point communication interfaces and/orprotocol(s), such as the NV-Link high-speed interconnect, orinterconnect protocols known in the art.

In one embodiment, the one or more parallel processor(s) 2712incorporate circuitry optimized for graphics and video processing,including, for example, video output circuitry, and constitutes agraphics processing unit (GPU). In another embodiment, the one or moreparallel processor(s) 2712 incorporate circuitry optimized for generalpurpose processing, while preserving the underlying computationalarchitecture, described in greater detail herein. In yet anotherembodiment, components of the computing system 2700 may be integratedwith one or more other system elements on a single integrated circuit.For example, the one or more parallel processor(s), 2712 memory hub2705, processor(s) 2702, and I/O hub 2707 can be integrated into asystem on chip (SoC) integrated circuit. Alternatively, the componentsof the computing system 2700 can be integrated into a single package toform a system in package (SIP) configuration. In one embodiment at leasta portion of the components of the computing system 2700 can beintegrated into a multi-chip module (MCM), which can be interconnectedwith other multi-chip modules into a modular computing system.

It will be appreciated that the computing system 2700 shown herein isillustrative and that variations and modifications are possible. Theconnection topology, including the number and arrangement of bridges,the number of processor(s) 2702, and the number of parallel processor(s)2712, may be modified as desired. For instance, in some embodiments,system memory 2704 is connected to the processor(s) 2702 directly ratherthan through a bridge, while other devices communicate with systemmemory 2704 via the memory hub 2705 and the processor(s) 2702. In otheralternative topologies, the parallel processor(s) 2712 are connected tothe I/O hub 2707 or directly to one of the one or more processor(s)2702, rather than to the memory hub 2705. In other embodiments, the I/Ohub 2707 and memory hub 2705 may be integrated into a single chip. Someembodiments may include two or more sets of processor(s) 2702 attachedvia multiple sockets, which can couple with two or more instances of theparallel processor(s) 2712.

Some of the particular components shown herein are optional and may notbe included in all implementations of the computing system 2700. Forexample, any number of add-in cards or peripherals may be supported, orsome components may be eliminated. Furthermore, some architectures mayuse different terminology for components similar to those illustrated inFIG. 27. For example, the memory hub 2705 may be referred to as aNorthbridge in some architectures, while the I/O hub 2707 may bereferred to as a Southbridge.

FIG. 28A illustrates a parallel processor 2800, according to anembodiment. The various components of the parallel processor 2800 may beimplemented using one or more integrated circuit devices, such asprogrammable processors, application specific integrated circuits(ASICs), or field programmable gate arrays (FPGA). The illustratedparallel processor 2800 is a variant of the one or more parallelprocessor(s) 28712 shown in FIG. 27, according to an embodiment.

In one embodiment the parallel processor 2800 includes a parallelprocessing unit 2802. The parallel processing unit includes an I/O unit2804 that enables communication with other devices, including otherinstances of the parallel processing unit 2802. The I/O unit 2804 may bedirectly connected to other devices. In one embodiment the I/O unit 2804connects with other devices via the use of a hub or switch interface,such as memory hub 2705. The connections between the memory hub 2705 andthe I/O unit 2804 form a communication link 2713. Within the parallelprocessing unit 2802, the I/O unit 2804 connects with a host interface2806 and a memory crossbar 2816, where the host interface 2806 receivescommands directed to performing processing operations and the memorycrossbar 2816 receives commands directed to performing memoryoperations.

When the host interface 2806 receives a command buffer via the I/O unit2804, the host interface 2806 can direct work operations to performthose commands to a front end 2808. In one embodiment the front end 2808couples with a scheduler 2810, which is configured to distributecommands or other work items to a processing cluster array 2812. In oneembodiment the scheduler 2810 ensures that the processing cluster array2812 is properly configured and in a valid state before tasks aredistributed to the processing clusters of the processing cluster array2812. In one embodiment the scheduler 2810 is implemented via firmwarelogic executing on a microcontroller. The microcontroller implementedscheduler 2810 is configurable to perform complex scheduling and workdistribution operations at coarse and fine granularity, enabling rapidpreemption and context switching of threads executing on the processingarray 2812. In one embodiment, the host software can prove workloads forscheduling on the processing array 2812 via one of multiple graphicsprocessing doorbells. The workloads can then be automaticallydistributed across the processing array 2812 by the scheduler 2810 logicwithin the scheduler microcontroller.

The processing cluster array 2812 can include up to “N” processingclusters (e.g., cluster 2814A, cluster 2814B, through cluster 2814N).Each cluster 2814A-2814N of the processing cluster array 2812 canexecute a large number of concurrent threads. The scheduler 2810 canallocate work to the clusters 2814A-2814N of the processing clusterarray 2812 using various scheduling and/or work distribution algorithms,which may vary depending on the workload arising for each type ofprogram or computation. The scheduling can be handled dynamically by thescheduler 2810, or can be assisted in part by compiler logic duringcompilation of program logic configured for execution by the processingcluster array 2812. In one embodiment, different clusters 2814A-2814N ofthe processing cluster array 2812 can be allocated for processingdifferent types of programs or for performing different types ofcomputations.

The processing cluster array 2812 can be configured to perform varioustypes of parallel processing operations. In one embodiment theprocessing cluster array 2812 is configured to perform general-purposeparallel compute operations. For example, the processing cluster array2812 can include logic to execute processing tasks including filteringof video and/or audio data, performing modeling operations, includingphysics operations, and performing data transformations.

In one embodiment the processing cluster array 2812 is configured toperform parallel graphics processing operations. In embodiments in whichthe parallel processor 2800 is configured to perform graphics processingoperations, the processing cluster array 2812 can include additionallogic to support the execution of such graphics processing operations,including, but not limited to texture sampling logic to perform textureoperations, as well as tessellation logic and other vertex processinglogic. Additionally, the processing cluster array 2812 can be configuredto execute graphics processing related shader programs such as, but notlimited to vertex shaders, tessellation shaders, geometry shaders, andpixel shaders. The parallel processing unit 2802 can transfer data fromsystem memory via the I/O unit 2804 for processing. During processingthe transferred data can be stored to on-chip memory (e.g., parallelprocessor memory 2822) during processing, then written back to systemmemory.

In one embodiment, when the parallel processing unit 2802 is used toperform graphics processing, the scheduler 2810 can be configured todivide the processing workload into approximately equal sized tasks, tobetter enable distribution of the graphics processing operations tomultiple clusters 2814A-2814N of the processing cluster array 2812. Insome embodiments, portions of the processing cluster array 2812 can beconfigured to perform different types of processing. For example a firstportion may be configured to perform vertex shading and topologygeneration, a second portion may be configured to perform tessellationand geometry shading, and a third portion may be configured to performpixel shading or other screen space operations, to produce a renderedimage for display. Intermediate data produced by one or more of theclusters 2814A-2814N may be stored in buffers to allow the intermediatedata to be transmitted between clusters 2814A-2814N for furtherprocessing.

During operation, the processing cluster array 2812 can receiveprocessing tasks to be executed via the scheduler 2810, which receivescommands defining processing tasks from front end 2808. For graphicsprocessing operations, processing tasks can include indices of data tobe processed, e.g., surface (patch) data, primitive data, vertex data,and/or pixel data, as well as state parameters and commands defining howthe data is to be processed (e.g., what program is to be executed). Thescheduler 2810 may be configured to fetch the indices corresponding tothe tasks or may receive the indices from the front end 2808. The frontend 2808 can be configured to ensure the processing cluster array 2812is configured to a valid state before the workload specified by incomingcommand buffers (e.g., batch-buffers, push buffers, etc.) is initiated.

Each of the one or more instances of the parallel processing unit 2802can couple with parallel processor memory 2822. The parallel processormemory 2822 can be accessed via the memory crossbar 2816, which canreceive memory requests from the processing cluster array 2812 as wellas the I/O unit 2804. The memory crossbar 2816 can access the parallelprocessor memory 2822 via a memory interface 2818. The memory interface2818 can include multiple partition units (e.g., partition unit 2820A,partition unit 2820B, through partition unit 2820N) that can each coupleto a portion (e.g., memory unit) of parallel processor memory 2822. Inone implementation the number of partition units 2820A-2820N isconfigured to be equal to the number of memory units, such that a firstpartition unit 2820A has a corresponding first memory unit 2824A, asecond partition unit 2820B has a corresponding memory unit 2824B, andan Nth partition unit 2820N has a corresponding Nth memory unit 2824N.In other embodiments, the number of partition units 2820A-2820N may notbe equal to the number of memory devices.

In various embodiments, the memory units 2824A-2824N can include varioustypes of memory devices, including dynamic random access memory (DRAM)or graphics random access memory, such as synchronous graphics randomaccess memory (SGRAM), including graphics double data rate (GDDR)memory. In one embodiment, the memory units 2824A-2824N may also include3D stacked memory, including but not limited to high bandwidth memory(HBM). Persons skilled in the art will appreciate that the specificimplementation of the memory units 2824A-2824N can vary, and can beselected from one of various conventional designs. Render targets, suchas frame buffers or texture maps may be stored across the memory units2824A-2824N, allowing partition units 2820A-2820N to write portions ofeach render target in parallel to efficiently use the availablebandwidth of parallel processor memory 2822. In some embodiments, alocal instance of the parallel processor memory 2822 may be excluded infavor of a unified memory design that utilizes system memory inconjunction with local cache memory.

In one embodiment, any one of the clusters 2814A-2814N of the processingcluster array 2812 can process data that will be written to any of thememory units 2824A-2824N within parallel processor memory 2822. Thememory crossbar 2816 can be configured to transfer the output of eachcluster 2814A-2814N to any partition unit 2820A-2820N or to anothercluster 2814A-2814N, which can perform additional processing operationson the output. Each cluster 2814A-2814N can communicate with the memoryinterface 2818 through the memory crossbar 2816 to read from or write tovarious external memory devices. In one embodiment the memory crossbar2816 has a connection to the memory interface 2818 to communicate withthe I/O unit 2804, as well as a connection to a local instance of theparallel processor memory 2822, enabling the processing units within thedifferent processing clusters 2814A-2814N to communicate with systemmemory or other memory that is not local to the parallel processing unit2802. In one embodiment the memory crossbar 2816 can use virtualchannels to separate traffic streams between the clusters 2814A-2814Nand the partition units 2820A-2820N.

While a single instance of the parallel processing unit 2802 isillustrated within the parallel processor 2800, any number of instancesof the parallel processing unit 2802 can be included. For example,multiple instances of the parallel processing unit 2802 can be providedon a single add-in card, or multiple add-in cards can be interconnected.The different instances of the parallel processing unit 2802 can beconfigured to inter-operate even if the different instances havedifferent numbers of processing cores, different amounts of localparallel processor memory, and/or other configuration differences. Forexample and in one embodiment, some instances of the parallel processingunit 2802 can include higher precision floating point units relative toother instances. Systems incorporating one or more instances of theparallel processing unit 2802 or the parallel processor 2800 can beimplemented in a variety of configurations and form factors, includingbut not limited to desktop, laptop, or handheld personal computers,servers, workstations, game consoles, and/or embedded systems.

FIG. 28B is a block diagram of a partition unit 2820, according to anembodiment. In one embodiment the partition unit 2820 is an instance ofone of the partition units 2820A-2820N of FIG. 28A. As illustrated, thepartition unit 2820 includes an L2 cache 2821, a frame buffer interface2825, and a ROP 2826 (raster operations unit). The L2 cache 2821 is aread/write cache that is configured to perform load and store operationsreceived from the memory crossbar 2816 and ROP 2826. Read misses andurgent write-back requests are output by L2 cache 2821 to frame bufferinterface 2825 for processing. Updates can also be sent to the framebuffer via the frame buffer interface 2825 for processing. In oneembodiment the frame buffer interface 2825 interfaces with one of thememory units in parallel processor memory, such as the memory units2824A-2824N of FIG. 28 (e.g., within parallel processor memory 2822).

In graphics applications, the ROP 2826 is a processing unit thatperforms raster operations such as stencil, z test, blending, and thelike. The ROP 2826 then outputs processed graphics data that is storedin graphics memory. In some embodiments the ROP 2826 includescompression logic to compress depth or color data that is written tomemory and decompress depth or color data that is read from memory. Thecompression logic can be lossless compression logic that makes use ofone or more of multiple compression algorithms. The type of compressionthat is performed by the ROP 2826 can vary based on the statisticalcharacteristics of the data to be compressed. For example, in oneembodiment, delta color compression is performed on depth and color dataon a per-tile basis.

In some embodiments, the ROP 2826 is included within each processingcluster (e.g., cluster 2814A-2814N of FIG. 28) instead of within thepartition unit 2820. In such embodiment, read and write requests forpixel data are transmitted over the memory crossbar 2816 instead ofpixel fragment data. The processed graphics data may be displayed on adisplay device, such as one of the one or more display device(s) 2710 ofFIG. 27, routed for further processing by the processor(s) 2702, orrouted for further processing by one of the processing entities withinthe parallel processor 2800 of FIG. 28A.

FIG. 28C is a block diagram of a processing cluster 2814 within aparallel processing unit, according to an embodiment. In one embodimentthe processing cluster is an instance of one of the processing clusters2814A-2814N of FIG. 28. The processing cluster 2814 can be configured toexecute many threads in parallel, where the term “thread” refers to aninstance of a particular program executing on a particular set of inputdata. In some embodiments, single-instruction, multiple-data (SIMD)instruction issue techniques are used to support parallel execution of alarge number of threads without providing multiple independentinstruction units. In other embodiments, single-instruction,multiple-thread (SIMT) techniques are used to support parallel executionof a large number of generally synchronized threads, using a commoninstruction unit configured to issue instructions to a set of processingengines within each one of the processing clusters. Unlike a SIMDexecution regime, where all processing engines typically executeidentical instructions, SIMT execution allows different threads to morereadily follow divergent execution paths through a given thread program.Persons skilled in the art will understand that a SIMD processing regimerepresents a functional subset of a SIMT processing regime.

Operation of the processing cluster 2814 can be controlled via apipeline manager 2832 that distributes processing tasks to SIMT parallelprocessors. The pipeline manager 2832 receives instructions from thescheduler 2810 of FIG. 28 and manages execution of those instructionsvia a graphics multiprocessor 2834 and/or a texture unit 2836. Theillustrated graphics multiprocessor 2834 is an exemplary instance of aSIMT parallel processor. However, various types of SIMT parallelprocessors of differing architectures may be included within theprocessing cluster 2814. One or more instances of the graphicsmultiprocessor 2834 can be included within a processing cluster 2814.The graphics multiprocessor 2834 can process data and a data crossbar2840 can be used to distribute the processed data to one of multiplepossible destinations, including other shader units. The pipelinemanager 2832 can facilitate the distribution of processed data byspecifying destinations for processed data to be distributed vis thedata crossbar 2840.

Each graphics multiprocessor 2834 within the processing cluster 2814 caninclude an identical set of functional execution logic (e.g., arithmeticlogic units, load-store units, etc.). The functional execution logic canbe configured in a pipelined manner in which new instructions can beissued before previous instructions are complete. The functionalexecution logic supports a variety of operations including integer andfloating point arithmetic, comparison operations, Boolean operations,bit-shifting, and computation of various algebraic functions. In oneembodiment the same functional-unit hardware can be leveraged to performdifferent operations and any combination of functional units may bepresent.

The instructions transmitted to the processing cluster 2814 constitutesa thread. A set of threads executing across the set of parallelprocessing engines is a thread group. A thread group executes the sameprogram on different input data. Each thread within a thread group canbe assigned to a different processing engine within a graphicsmultiprocessor 2834. A thread group may include fewer threads than thenumber of processing engines within the graphics multiprocessor 2834.When a thread group includes fewer threads than the number of processingengines, one or more of the processing engines may be idle during cyclesin which that thread group is being processed. A thread group may alsoinclude more threads than the number of processing engines within thegraphics multiprocessor 2834. When the thread group includes morethreads than the number of processing engines within the graphicsmultiprocessor 2834, processing can be performed over consecutive clockcycles. In one embodiment multiple thread groups can be executedconcurrently on a graphics multiprocessor 2834.

In one embodiment the graphics multiprocessor 2834 includes an internalcache memory to perform load and store operations. In one embodiment,the graphics multiprocessor 2834 can forego an internal cache and use acache memory (e.g., L1 cache 308) within the processing cluster 2814.Each graphics multiprocessor 2834 also has access to L2 caches withinthe partition units (e.g., partition units 2820A-2820N of FIG. 28) thatare shared among all processing clusters 2814 and may be used totransfer data between threads. The graphics multiprocessor 2834 may alsoaccess off-chip global memory, which can include one or more of localparallel processor memory and/or system memory. Any memory external tothe parallel processing unit 2802 may be used as global memory.Embodiments in which the processing cluster 2814 includes multipleinstances of the graphics multiprocessor 2834 can share commoninstructions and data, which may be stored in the L1 cache 2908.

Each processing cluster 2814 may include an MMU 2845 (memory managementunit) that is configured to map virtual addresses into physicaladdresses. In other embodiments, one or more instances of the MMU 2845may reside within the memory interface 2818 of FIG. 28. The MMU 2845includes a set of page table entries (PTEs) used to map a virtualaddress to a physical address of a tile (talk more about tiling) andoptionally a cache line index. The MMU 2845 may include addresstranslation lookaside buffers (TLB) or caches that may reside within thegraphics multiprocessor 2834 or the L1 cache or processing cluster 2814.The physical address is processed to distribute surface data accesslocality to allow efficient request interleaving among partition units.The cache line index may be used to determine whether a request for acache line is a hit or miss.

In graphics and computing applications, a processing cluster 2814 may beconfigured such that each graphics multiprocessor 2834 is coupled to atexture unit 2836 for performing texture mapping operations, e.g.,determining texture sample positions, reading texture data, andfiltering the texture data. Texture data is read from an internaltexture L1 cache (not shown) or in some embodiments from the L1 cachewithin graphics multiprocessor 2834 and is fetched from an L2 cache,local parallel processor memory, or system memory, as needed. Eachgraphics multiprocessor 2834 outputs processed tasks to the datacrossbar 2840 to provide the processed task to another processingcluster 2814 for further processing or to store the processed task in anL2 cache, local parallel processor memory, or system memory via thememory crossbar 2816. A preROP 2842 (pre-raster operations unit) isconfigured to receive data from graphics multiprocessor 2834, directdata to ROP units, which may be located with partition units asdescribed herein (e.g., partition units 2820A-2820N of FIG. 28). ThepreROP 2842 unit can perform optimizations for color blending, organizepixel color data, and perform address translations.

It will be appreciated that the core architecture described herein isillustrative and that variations and modifications are possible. Anynumber of processing units, e.g., graphics multiprocessor 2834, textureunits 2836, preROPs 2842, etc., may be included within a processingcluster 2814. Further, while only one processing cluster 2814 is shown,a parallel processing unit as described herein may include any number ofinstances of the processing cluster 2814. In one embodiment, eachprocessing cluster 2814 can be configured to operate independently ofother processing clusters 2814 using separate and distinct processingunits, L1 caches, etc.

FIG. 28D shows a graphics multiprocessor 2834, according to oneembodiment. In such embodiment the graphics multiprocessor 2834 coupleswith the pipeline manager 2832 of the processing cluster 2814. Thegraphics multiprocessor 2834 has an execution pipeline including but notlimited to an instruction cache 2852, an instruction unit 2854, anaddress mapping unit 2856, a register file 2858, one or more generalpurpose graphics processing unit (GPGPU) cores 2862, and one or moreload/store units 2866. The GPGPU cores 2862 and load/store units 2866are coupled with cache memory 2872 and shared memory 2870 via a memoryand cache interconnect 2868.

In one embodiment, the instruction cache 2852 receives a stream ofinstructions to execute from the pipeline manager 2832. The instructionsare cached in the instruction cache 2852 and dispatched for execution bythe instruction unit 2854. The instruction unit 2854 can dispatchinstructions as thread groups (e.g., warps), with each thread of thethread group assigned to a different execution unit within GPGPU core2862. An instruction can access any of a local, shared, or globaladdress space by specifying an address within a unified address space.The address mapping unit 2856 can be used to translate addresses in theunified address space into a distinct memory address that can beaccessed by the load/store units 2866.

The register file 2858 provides a set of registers for the functionalunits of the graphics multiprocessor 324. The register file 2858provides temporary storage for operands connected to the data paths ofthe functional units (e.g., GPGPU cores 2862, load/store units 2866) ofthe graphics multiprocessor 2924. In one embodiment, the register file2858 is divided between each of the functional units such that eachfunctional unit is allocated a dedicated portion of the register file2858. In one embodiment, the register file 2858 is divided between thedifferent warps being executed by the graphics multiprocessor 2924.

The GPGPU cores 2862 can each include floating point units (FPUs) and/orinteger arithmetic logic units (ALUs) that are used to executeinstructions of the graphics multiprocessor 2924. The GPGPU cores 2862can be similar in architecture or can differ in architecture, accordingto embodiments. For example and in one embodiment, a first portion ofthe GPGPU cores 2862 include a single precision FPU and an integer ALUwhile a second portion of the GPGPU cores include a double precisionFPU. In one embodiment the FPUs can implement the IEEE 754-2008 standardfor floating point arithmetic or enable variable precision floatingpoint arithmetic. The graphics multiprocessor 2924 can additionallyinclude one or more fixed function or special function units to performspecific functions such as copy rectangle or pixel blending operations.In one embodiment one or more of the GPGPU cores can also include fixedor special function logic.

In one embodiment the GPGPU cores 2862 include SIMD logic capable ofperforming a single instruction on multiple sets of data. In oneembodiment GPGPU cores 2862 can physically execute SIMD4, SIMD8, andSIMD16 instructions and logically execute SIMD1, SIMD2, and SIMD32instructions. The SIMD instructions for the GPGPU cores can be generatedat compile time by a shader compiler or automatically generated whenexecuting programs written and compiled for single program multiple data(SPMD) or SIMT architectures. Multiple threads of a program configuredfor the SIMT execution model can executed via a single SIMD instruction.For example and in one embodiment, eight SIMT threads that perform thesame or similar operations can be executed in parallel via a singleSIMD8 logic unit.

The memory and cache interconnect 2868 is an interconnect network thatconnects each of the functional units of the graphics multiprocessor2924 to the register file 2858 and to the shared memory 2870. In oneembodiment, the memory and cache interconnect 2868 is a crossbarinterconnect that allows the load/store unit 2866 to implement load andstore operations between the shared memory 2870 and the register file2858. The register file 2858 can operate at the same frequency as theGPGPU cores 2862, thus data transfer between the GPGPU cores 2862 andthe register file 2858 is very low latency. The shared memory 2870 canbe used to enable communication between threads that execute on thefunctional units within the graphics multiprocessor 2834. The cachememory 2872 can be used as a data cache for example, to cache texturedata communicated between the functional units and the texture unit2836. The shared memory 2870 can also be used as a program managedcached. Threads executing on the GPGPU cores 2862 can programmaticallystore data within the shared memory in addition to the automaticallycached data that is stored within the cache memory 2872.

FIGS. 29A-29B illustrate additional graphics multiprocessors, accordingto embodiments. The illustrated graphics multiprocessors 2925, 2950 arevariants of the graphics multiprocessor 2934 of FIG. 29C. Theillustrated graphics multiprocessors 2925, 2950 can be configured as astreaming multiprocessor (SM) capable of simultaneous execution of alarge number of execution threads.

FIG. 29A shows a graphics multiprocessor 2925 according to an additionalembodiment. The graphics multiprocessor 2925 includes multipleadditional instances of execution resource units relative to thegraphics multiprocessor 2834 of FIG. 28D. For example, the graphicsmultiprocessor 2925 can include multiple instances of the instructionunit 2932A-2932B, register file 2934A-2934B, and texture unit(s)2944A-2944B. The graphics multiprocessor 2925 also includes multiplesets of graphics or compute execution units (e.g., GPGPU core2936A-2936B, GPGPU core 2937A-2937B, GPGPU core 2938A-2938B) andmultiple sets of load/store units 2940A-2940B. In one embodiment theexecution resource units have a common instruction cache 2930, textureand/or data cache memory 2942, and shared memory 2946.

The various components can communicate via an interconnect fabric 2927.In one embodiment the interconnect fabric 2927 includes one or morecrossbar switches to enable communication between the various componentsof the graphics multiprocessor 2925. In one embodiment the interconnectfabric 2927 is a separate, high-speed network fabric layer upon whicheach component of the graphics multiprocessor 2925 is stacked. Thecomponents of the graphics multiprocessor 2925 communicate with remotecomponents via the interconnect fabric 2927. For example, the GPGPUcores 2936A-2936B, 2937A-2937B, and 2937A-2938B can each communicatewith shared memory 2946 via the interconnect fabric 2927. Theinterconnect fabric 2927 can arbitrate communication within the graphicsmultiprocessor 2925 to ensure a fair bandwidth allocation betweencomponents.

FIG. 29B shows a graphics multiprocessor 2950 according to an additionalembodiment. The graphics processor includes multiple sets of executionresources 2956A-2956D, where each set of execution resource includesmultiple instruction units, register files, GPGPU cores, and load storeunits, as illustrated in FIG. 28D and FIG. 29A. The execution resources2956A-2956D can work in concert with texture unit(s) 2960A-2960D fortexture operations, while sharing an instruction cache 2954, and sharedmemory 2962. In one embodiment the execution resources 2956A-2956D canshare an instruction cache 2954 and shared memory 2962, as well asmultiple instances of a texture and/or data cache memory 2958A-2958B.The various components can communicate via an interconnect fabric 2952similar to the interconnect fabric 2927 of FIG. 29A.

Persons skilled in the art will understand that the architecturedescribed in FIGS. 27, 28A-28D, and 29A-29B are descriptive and notlimiting as to the scope of the present embodiments. Thus, thetechniques described herein may be implemented on any properlyconfigured processing unit, including, without limitation, one or moremobile application processors, one or more desktop or server centralprocessing units (CPUs) including multi-core CPUs, one or more parallelprocessing units, such as the parallel processing unit 2802 of FIG. 28,as well as one or more graphics processors or special purpose processingunits, without departure from the scope of the embodiments describedherein.

In some embodiments a parallel processor or GPGPU as described herein iscommunicatively coupled to host/processor cores to accelerate graphicsoperations, machine-learning operations, pattern analysis operations,and various general purpose GPU (GPGPU) functions. The GPU may becommunicatively coupled to the host processor/cores over a bus or otherinterconnect (e.g., a high speed interconnect such as PCIe or NVLink).In other embodiments, the GPU may be integrated on the same package orchip as the cores and communicatively coupled to the cores over aninternal processor bus/interconnect (i.e., internal to the package orchip). Regardless of the manner in which the GPU is connected, theprocessor cores may allocate work to the GPU in the form of sequences ofcommands/instructions contained in a work descriptor. The GPU then usesdedicated circuitry/logic for efficiently processing thesecommands/instructions.

Techniques for GPU to Host Processor Interconnection

FIG. 30A illustrates an exemplary architecture in which a plurality ofGPUs 3010-3013 are communicatively coupled to a plurality of multi-coreprocessors 3005-3006 over high-speed links 3040-3043 (e.g., buses,point-to-point interconnects, etc.). In one embodiment, the high-speedlinks 3040-3043 support a communication throughput of 4 GB/s, 30 GB/s,80 GB/s or higher, depending on the implementation. Various interconnectprotocols may be used including, but not limited to, PCIe 4.0 or 5.0 andNVLink 2.0. However, the underlying principles of the invention are notlimited to any particular communication protocol or throughput.

In addition, in one embodiment, two or more of the GPUs 3010-3013 areinterconnected over high-speed links 3044-3045, which may be implementedusing the same or different protocols/links than those used forhigh-speed links 3040-3043. Similarly, two or more of the multi-coreprocessors 3005-3006 may be connected over high speed link 3033 whichmay be symmetric multi-processor (SMP) buses operating at 20 GB/s, 30GB/s, 120 GB/s or higher. Alternatively, all communication between thevarious system components shown in FIG. 30A may be accomplished usingthe same protocols/links (e.g., over a common interconnection fabric).As mentioned, however, the underlying principles of the invention arenot limited to any particular type of interconnect technology.

In one embodiment, each multi-core processor 3005-3006 iscommunicatively coupled to a processor memory 3001-3002, via memoryinterconnects 3030-3031, respectively, and each GPU 3010-3013 iscommunicatively coupled to GPU memory 3020-3023 over GPU memoryinterconnects 3050-3053, respectively. The memory interconnects3030-3031 and 3050-3053 may utilize the same or different memory accesstechnologies. By way of example, and not limitation, the processormemories 3001-3002 and GPU memories 3020-3023 may be volatile memoriessuch as dynamic random access memories (DRAMs) (including stackedDRAMs), Graphics DDR SDRAM (GDDR) (e.g., GDDR5, GDDR6), or HighBandwidth Memory (HBM) and/or may be non-volatile memories such as 3DXPoint or Nano-Ram. In one embodiment, some portion of the memories maybe volatile memory and another portion may be non-volatile memory (e.g.,using a two-level memory (2LM) hierarchy).

As described below, although the various processors 3005-3006 and GPUs3010-3013 may be physically coupled to a particular memory 3001-3002,3020-3023, respectively, a unified memory architecture may beimplemented in which the same virtual system address space (alsoreferred to as the “effective address” space) is distributed among allof the various physical memories. For example, processor memories3001-3002 may each comprise 64 GB of the system memory address space andGPU memories 3020-3023 may each comprise 32 GB of the system memoryaddress space (resulting in a total of 256 GB addressable memory in thisexample).

FIG. 30B illustrates additional details for an interconnection between amulti-core processor 3007 and a graphics acceleration module 3046 inaccordance with one embodiment. The graphics acceleration module 3046may include one or more GPU chips integrated on a line card which iscoupled to the processor 3007 via the high-speed link 3040.Alternatively, the graphics acceleration module 3046 may be integratedon the same package or chip as the processor 3007.

The illustrated processor 3007 includes a plurality of cores3060A-3060D, each with a translation lookaside buffer 3061A-3061D andone or more caches 3062A-3062D. The cores may include various othercomponents for executing instructions and processing data which are notillustrated to avoid obscuring the underlying principles of theinvention (e.g., instruction fetch units, branch prediction units,decoders, execution units, reorder buffers, etc.). The caches3062A-3062D may comprise level 1 (L1) and level 2 (L2) caches. Inaddition, one or more shared caches 3026 may be included in the cachinghierarchy and shared by sets of the cores 3060A-3060D. For example, oneembodiment of the processor 3007 includes 24 cores, each with its own L1cache, twelve shared L2 caches, and twelve shared L3 caches. In thisembodiment, one of the L2 and L3 caches are shared by two adjacentcores. The processor 3007 and the graphics accelerator integrationmodule 3046 connect with system memory 3041, which may include processormemories 3001-3002

Coherency is maintained for data and instructions stored in the variouscaches 3062A-3062D, 3056 and system memory 3041 via inter-corecommunication over a coherence bus 3064. For example, each cache mayhave cache coherency logic/circuitry associated therewith to communicateto over the coherence bus 3064 in response to detected reads or writesto particular cache lines. In one implementation, a cache snoopingprotocol is implemented over the coherence bus 3064 to snoop cacheaccesses. Cache snooping/coherency techniques are well understood bythose of skill in the art and will not be described in detail here toavoid obscuring the underlying principles of the invention.

In one embodiment, a proxy circuit 3025 communicatively couples thegraphics acceleration module 3046 to the coherence bus 3064, allowingthe graphics acceleration module 3046 to participate in the cachecoherence protocol as a peer of the cores. In particular, an interface3035 provides connectivity to the proxy circuit 3025 over high-speedlink 3040 (e.g., a PCIe bus, NVLink, etc.) and an interface 3037connects the graphics acceleration module 3046 to the link 3040.

In one implementation, an accelerator integration circuit 3036 providescache management, memory access, context management, and interruptmanagement services on behalf of a plurality of graphics processingengines 3031, 3032, N of the graphics acceleration module 3046. Thegraphics processing engines 3031, 3032, N may each comprise a separategraphics processing unit (GPU). Alternatively, the graphics processingengines 3031, 3032, N may comprise different types of graphicsprocessing engines within a GPU such as graphics execution units, mediaprocessing engines (e.g., video encoders/decoders), samplers, and blitengines. In other words, the graphics acceleration module may be a GPUwith a plurality of graphics processing engines 3031-3032, N or thegraphics processing engines 3031-3032, N may be individual GPUsintegrated on a common package, line card, or chip.

In one embodiment, the accelerator integration circuit 3036 includes amemory management unit (MMU) 3039 for performing various memorymanagement functions such as virtual-to-physical memory translations(also referred to as effective-to-real memory translations) and memoryaccess protocols for accessing system memory 3041. The MMU 3039 may alsoinclude a translation lookaside buffer (TLB) (not shown) for caching thevirtual/effective to physical/real address translations. In oneimplementation, a cache 3038 stores commands and data for efficientaccess by the graphics processing engines 3031-3032, N. In oneembodiment, the data stored in cache 3038 and graphics memories3033-3034, N is kept coherent with the core caches 3062A-3062D, 3056 andsystem memory 3011. As mentioned, this may be accomplished via proxycircuit 3025 which takes part in the cache coherency mechanism on behalfof cache 3038 and memories 3033-3034, N (e.g., sending updates to thecache 3038 related to modifications/accesses of cache lines on processorcaches 3062A-3062D, 3056 and receiving updates from the cache 3038).

A set of registers 3045 store context data for threads executed by thegraphics processing engines 3031-3032, N and a context managementcircuit 3048 manages the thread contexts. For example, the contextmanagement circuit 3048 may perform save and restore operations to saveand restore contexts of the various threads during contexts switches(e.g., where a first thread is saved and a second thread is stored sothat the second thread can be execute by a graphics processing engine).For example, on a context switch, the context management circuit 3048may store current register values to a designated region in memory(e.g., identified by a context pointer). It may then restore theregister values when returning to the context. In one embodiment, aninterrupt management circuit 3047 receives and processes interruptsreceived from system devices.

In one implementation, virtual/effective addresses from a graphicsprocessing engine 3031 are translated to real/physical addresses insystem memory 3011 by the MMU 3039. One embodiment of the acceleratorintegration circuit 3036 supports multiple (e.g., 4, 8, 16) graphicsaccelerator modules 3046 and/or other accelerator devices. The graphicsaccelerator module 3046 may be dedicated to a single applicationexecuted on the processor 3007 or may be shared between multipleapplications. In one embodiment, a virtualized graphics executionenvironment is presented in which the resources of the graphicsprocessing engines 3031-3032, N are shared with multiple applications orvirtual machines (VMs). The resources may be subdivided into “slices”which are allocated to different VMs and/or applications based on theprocessing requirements and priorities associated with the VMs and/orapplications.

Thus, the accelerator integration circuit acts as a bridge to the systemfor the graphics acceleration module 3046 and provides addresstranslation and system memory cache services. In addition, theaccelerator integration circuit 3036 may provide virtualizationfacilities for the host processor to manage virtualization of thegraphics processing engines, interrupts, and memory management.

Because hardware resources of the graphics processing engines 3031-3032,N are mapped explicitly to the real address space seen by the hostprocessor 3007, any host processor can address these resources directlyusing an effective address value. One function of the acceleratorintegration circuit 3036, in one embodiment, is the physical separationof the graphics processing engines 3031-3032, N so that they appear tothe system as independent units.

As mentioned, in the illustrated embodiment, one or more graphicsmemories 3033-3034, M are coupled to each of the graphics processingengines 3031-3032, N, respectively. The graphics memories 3033-3034, Mstore instructions and data being processed by each of the graphicsprocessing engines 3031-3032, N. The graphics memories 3033-3034, M maybe volatile memories such as DRAMs (including stacked DRAMs), GDDRmemory (e.g., GDDR5, GDDR6), or HBM, and/or may be non-volatile memoriessuch as 3D XPoint or Nano-Ram.

In one embodiment, to reduce data traffic over link 3040, biasingtechniques are used to ensure that the data stored in graphics memories3033-3034, M is data which will be used most frequently by the graphicsprocessing engines 3031-3032, N and preferably not used by the cores3060A-3060D (at least not frequently). Similarly, the biasing mechanismattempts to keep data needed by the cores (and preferably not thegraphics processing engines 3031-3032, N) within the caches 3062A-3062D,3056 of the cores and system memory 3011.

FIG. 30C illustrates another embodiment in which the acceleratorintegration circuit 3036 is integrated within the processor 3007. Inthis embodiment, the graphics processing engines 3031-3032, Ncommunicate directly over the high-speed link 3040 to the acceleratorintegration circuit 3036 via interface 3037 and interface 3035 (which,again, may be utilize any form of bus or interface protocol). Theaccelerator integration circuit 3036 may perform the same operations asthose described with respect to FIG. 30B, but potentially at a higherthroughput given its close proximity to the coherency bus 3062 andcaches 3062A-3062D, 3026.

One embodiment supports different programming models including adedicated-process programming model (no graphics acceleration modulevirtualization) and shared programming models (with virtualization). Thelatter may include programming models which are controlled by theaccelerator integration circuit 3036 and programming models which arecontrolled by the graphics acceleration module 3046.

In one embodiment of the dedicated process model, graphics processingengines 3031-3032, N are dedicated to a single application or processunder a single operating system. The single application can funnel otherapplication requests to the graphics engines 3031-3032, N, providingvirtualization within a VM/partition.

In the dedicated-process programming models, the graphics processingengines 3031-3032, N, may be shared by multiple VM/applicationpartitions. The shared models require a system hypervisor to virtualizethe graphics processing engines 3031-3032, N to allow access by eachoperating system. For single-partition systems without a hypervisor, thegraphics processing engines 3031-3032, N are owned by the operatingsystem. In both cases, the operating system can virtualize the graphicsprocessing engines 3031-3032, N to provide access to each process orapplication.

For the shared programming model, the graphics acceleration module 3046or an individual graphics processing engine 3031-3032, N selects aprocess element using a process handle. In one embodiment, processelements are stored in system memory 3011 and are addressable using theeffective address to real address translation techniques describedherein. The process handle may be an implementation-specific valueprovided to the host process when registering its context with thegraphics processing engine 3031-3032, N (that is, calling systemsoftware to add the process element to the process element linked list).The lower 16-bits of the process handle may be the offset of the processelement within the process element linked list.

FIG. 30D illustrates an exemplary accelerator integration slice 3090. Asused herein, a “slice” comprises a specified portion of the processingresources of the accelerator integration circuit 3036. Applicationeffective address space 3082 within system memory 3011 stores processelements 3083. In one embodiment, the process elements 3083 are storedin response to GPU invocations 3081 from applications 3080 executed onthe processor 3007. A process element 3083 contains the process statefor the corresponding application 3080. A work descriptor (WD) 3084contained in the process element 3083 can be a single job requested byan application or may contain a pointer to a queue of jobs. In thelatter case, the WD 3084 is a pointer to the job request queue in theapplication's address space 3082.

The graphics acceleration module 3046 and/or the individual graphicsprocessing engines 3031-3032, N can be shared by all or a subset of theprocesses in the system. Embodiments of the invention include aninfrastructure for setting up the process state and sending a WD 3084 toa graphics acceleration module 3046 to start a job in a virtualizedenvironment.

In one implementation, the dedicated-process programming model isimplementation-specific. In this model, a single process owns thegraphics acceleration module 3046 or an individual graphics processingengine 3031. Because the graphics acceleration module 3046 is owned by asingle process, the hypervisor initializes the accelerator integrationcircuit 3036 for the owning partition and the operating systeminitializes the accelerator integration circuit 3036 for the owningprocess at the time when the graphics acceleration module 3046 isassigned.

In operation, a WD fetch unit 3091 in the accelerator integration slice3090 fetches the next WD 3084 which includes an indication of the workto be done by one of the graphics processing engines of the graphicsacceleration module 3046. Data from the WD 3084 may be stored inregisters 3045 and used by the MMU 3039, interrupt management circuit3047 and/or context management circuit 3046 as illustrated. For example,one embodiment of the MMU 3039 includes segment/page walk circuitry foraccessing segment/page tables 3086 within the OS virtual address space3085. The interrupt management circuit 3047 may process interrupt events3092 received from the graphics acceleration module 3046. Whenperforming graphics operations, an effective address 3093 generated by agraphics processing engine 3031-3032, N is translated to a real addressby the MMU 3039.

In one embodiment, the same set of registers 3045 are duplicated foreach graphics processing engine 3031-3032, N and/or graphicsacceleration module 3046 and may be initialized by the hypervisor oroperating system. Each of these duplicated registers may be included inan accelerator integration slice 3090. Exemplary registers that may beinitialized by the hypervisor are shown in Table 1.

TABLE 1 Hypervisor Initialized Registers 1 Slice Control Register 2 RealAddress (RA) Scheduled Processes Area Pointer 3 Authority Mask OverrideRegister 4 Interrupt Vector Table Entry Offset 5 Interrupt Vector TableEntry Limit 6 State Register 7 Logical Partition ID 8 Real address (RA)Hypervisor Accelerator Utilization Record Pointer 9 Storage DescriptionRegister

Exemplary registers that may be initialized by the operating system areshown in Table 2.

TABLE 2 Operating System Initialized Registers 1 Process and ThreadIdentification 2 Effective Address (EA) Context Save/Restore Pointer 3Virtual Address (VA) Accelerator Utilization Record Pointer 4 VirtualAddress (VA) Storage Segment Table Pointer 5 Authority Mask 6 Workdescriptor

In one embodiment, each WD 3084 is specific to a particular graphicsacceleration module 3046 and/or graphics processing engine 3031-3032, N.It contains all the information a graphics processing engine 3031-3032,N requires to do its work or it can be a pointer to a memory locationwhere the application has set up a command queue of work to becompleted.

FIG. 30E illustrates additional details for one embodiment of a sharedmodel. This embodiment includes a hypervisor real address space 3098 inwhich a process element list 3099 is stored. The hypervisor real addressspace 3098 is accessible via a hypervisor 3096 which virtualizes thegraphics acceleration module engines for the operating system 3095.

The shared programming models allow for all or a subset of processesfrom all or a subset of partitions in the system to use a graphicsacceleration module 3046. There are two programming models where thegraphics acceleration module 3046 is shared by multiple processes andpartitions: time-sliced shared and graphics directed shared.

In this model, the system hypervisor 3096 owns the graphics accelerationmodule 3046 and makes its function available to all operating systems3095. For a graphics acceleration module 3046 to support virtualizationby the system hypervisor 3096, the graphics acceleration module 3046 mayadhere to the following requirements: 1) An application's job requestmust be autonomous (that is, the state does not need to be maintainedbetween jobs), or the graphics acceleration module 3046 must provide acontext save and restore mechanism. 2) An application's job request isguaranteed by the graphics acceleration module 3046 to complete in aspecified amount of time, including any translation faults, or thegraphics acceleration module 3046 provides the ability to preempt theprocessing of the job. 3) The graphics acceleration module 3046 must beguaranteed fairness between processes when operating in the directedshared programming model.

In one embodiment, for the shared model, the application 3080 isrequired to make an operating system 3095 system call with a graphicsacceleration module 3046 type, a work descriptor (WD), an authority maskregister (AMR) value, and a context save/restore area pointer (CSRP).The graphics acceleration module 3046 type describes the targetedacceleration function for the system call. The graphics accelerationmodule 3046 type may be a system-specific value. The WD is formattedspecifically for the graphics acceleration module 3046 and can be in theform of a graphics acceleration module 3046 command, an effectiveaddress pointer to a user-defined structure, an effective addresspointer to a queue of commands, or any other data structure to describethe work to be done by the graphics acceleration module 3046. In oneembodiment, the AMR value is the AMR state to use for the currentprocess. The value passed to the operating system is similar to anapplication setting the AMR. If the accelerator integration circuit 3036and graphics acceleration module 3046 implementations do not support aUser Authority Mask Override Register (UAMOR), the operating system mayapply the current UAMOR value to the AMR value before passing the AMR inthe hypervisor call. The hypervisor 3096 may optionally apply thecurrent Authority Mask Override Register (AMOR) value before placing theAMR into the process element 3083. In one embodiment, the CSRP is one ofthe registers 3045 containing the effective address of an area in theapplication's address space 3082 for the graphics acceleration module3046 to save and restore the context state. This pointer is optional ifno state is required to be saved between jobs or when a job ispreempted. The context save/restore area may be pinned system memory.

Upon receiving the system call, the operating system 3095 may verifythat the application 3080 has registered and been given the authority touse the graphics acceleration module 3046. The operating system 3095then calls the hypervisor 3096 with the information shown in Table 3.

TABLE 3 OS to Hypervisor Call Parameters 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)

Upon receiving the hypervisor call, the hypervisor 3096 verifies thatthe operating system 3095 has registered and been given the authority touse the graphics acceleration module 3046. The hypervisor 3096 then putsthe process element 3083 into the process element linked list for thecorresponding graphics acceleration module 3046 type. The processelement may include the information shown in Table 4.

TABLE 4 Process Element Information 1 A work descriptor (WD) 2 AnAuthority Mask Register (AMR) value (potentially masked). 3 An effectiveaddress (EA) Context Save/Restore Area Pointer (CSRP) 4 A process ID(PID) and optional thread ID (TID) 5 A virtual address (VA) acceleratorutilization record pointer (AURP) 6 The virtual address of the storagesegment table pointer (SSTP) 7 A logical interrupt service number (LISN)8 Interrupt vector table, derived from the hypervisor call parameters. 9A state register (SR) value 10 A logical partition ID (LPID) 11 A realaddress (RA) hypervisor accelerator utilization record pointer 12 TheStorage Descriptor Register (SDR)

In one embodiment, the hypervisor initializes a plurality of acceleratorintegration slice 3090 registers 3045.

As illustrated in FIG. 30F, one embodiment of the invention employs aunified memory addressable via a common virtual memory address spaceused to access the physical processor memories 3001-3002 and GPUmemories 3020-3023. In this implementation, operations executed on theGPUs 3010-3013 utilize the same virtual/effective memory address spaceto access the processors memories 3001-3002 and vice versa, therebysimplifying programmability. In one embodiment, a first portion of thevirtual/effective address space is allocated to the processor memory3001, a second portion to the second processor memory 3002, a thirdportion to the GPU memory 3020, and so on. The entire virtual/effectivememory space (sometimes referred to as the effective address space) isthereby distributed across each of the processor memories 3001-3002 andGPU memories 3020-3023, allowing any processor or GPU to access anyphysical memory with a virtual address mapped to that memory.

In one embodiment, bias/coherence management circuitry 3094A-3094Ewithin one or more of the MMUs 3039A-3039E ensures cache coherencebetween the caches of the host processors (e.g., 3005) and the GPUs3010-3013 and implements biasing techniques indicating the physicalmemories in which certain types of data should be stored. While multipleinstances of bias/coherence management circuitry 3094A-3094E areillustrated in FIG. 30F, the bias/coherence circuitry may be implementedwithin the MMU of one or more host processors 3005 and/or within theaccelerator integration circuit 3036.

One embodiment allows GPU-attached memory 3020-3023 to be mapped as partof system memory, and accessed using shared virtual memory (SVM)technology, but without suffering the typical performance drawbacksassociated with full system cache coherence. The ability to GPU-attachedmemory 3020-3023 to be accessed as system memory without onerous cachecoherence overhead provides a beneficial operating environment for GPUoffload. This arrangement allows the host processor 3005 software tosetup operands and access computation results, without the overhead oftradition I/O DMA data copies. Such traditional copies involve drivercalls, interrupts and memory mapped I/O (MMIO) accesses that are allinefficient relative to simple memory accesses. At the same time, theability to access GPU attached memory 3020-3023 without cache coherenceoverheads can be critical to the execution time of an offloadedcomputation. In cases with substantial streaming write memory traffic,for example, cache coherence overhead can significantly reduce theeffective write bandwidth seen by a GPU 3010-3013. The efficiency ofoperand setup, the efficiency of results access, and the efficiency ofGPU computation all play a role in determining the effectiveness of GPUoffload.

In one implementation, the selection of between GPU bias and hostprocessor bias is driven by a bias tracker data structure. A bias tablemay be used, for example, which may be a page-granular structure (i.e.,controlled at the granularity of a memory page) that includes 1 or 2bits per GPU-attached memory page. The bias table may be implemented ina stolen memory range of one or more GPU-attached memories 3020-3023,with or without a bias cache in the GPU 3010-3013 (e.g., to cachefrequently/recently used entries of the bias table). Alternatively, theentire bias table may be maintained within the GPU.

In one implementation, the bias table entry associated with each accessto the GPU-attached memory 3020-3023 is accessed prior the actual accessto the GPU memory, causing the following operations. First, localrequests from the GPU 3010-3013 that find their page in GPU bias areforwarded directly to a corresponding GPU memory 3020-3023. Localrequests from the GPU that find their page in host bias are forwarded tothe processor 3005 (e.g., over a high-speed link as discussed above). Inone embodiment, requests from the processor 3005 that find the requestedpage in host processor bias complete the request like a normal memoryread. Alternatively, requests directed to a GPU-biased page may beforwarded to the GPU 3010-3013. The GPU may then transition the page toa host processor bias if it is not currently using the page.

The bias state of a page can be changed either by a software-basedmechanism, a hardware-assisted software-based mechanism, or, for alimited set of cases, a purely hardware-based mechanism.

One mechanism for changing the bias state employs an API call (e.g.OpenCL), which, in turn, calls the GPU's device driver which, in turn,sends a message (or enqueues a command descriptor) to the GPU directingit to change the bias state and, for some transitions, perform a cacheflushing operation in the host. The cache flushing operation is requiredfor a transition from host processor 3005 bias to GPU bias, but is notrequired for the opposite transition.

In one embodiment, cache coherency is maintained by temporarilyrendering GPU-biased pages uncacheable by the host processor 3005. Toaccess these pages, the processor 3005 may request access from the GPU3010 which may or may not grant access right away, depending on theimplementation. Thus, to reduce communication between the processor 3005and GPU 3010 it is beneficial to ensure that GPU-biased pages are thosewhich are required by the GPU but not the host processor 3005 and viceversa.

Graphics Processing Pipeline

FIG. 31 illustrates a graphics processing pipeline 3100, according to anembodiment. In one embodiment a graphics processor can implement theillustrated graphics processing pipeline 3100. The graphics processorcan be included within the parallel processing subsystems as describedherein, such as the parallel processor 2800 of FIG. 28, which, in oneembodiment, is a variant of the parallel processor(s) 2712 of FIG. 27.The various parallel processing systems can implement the graphicsprocessing pipeline 3100 via one or more instances of the parallelprocessing unit (e.g., parallel processing unit 2802 of FIG. 28) asdescribed herein. For example, a shader unit (e.g., graphicsmultiprocessor 2834 of FIG. 29) may be configured to perform thefunctions of one or more of a vertex processing unit 3104, atessellation control processing unit 3108, a tessellation evaluationprocessing unit 3112, a geometry processing unit 3116, and afragment/pixel processing unit 3124. The functions of data assembler3102, primitive assemblers 3106, 3114, 3118, tessellation unit 3110,rasterizer 3122, and raster operations unit 3126 may also be performedby other processing engines within a processing cluster (e.g.,processing cluster 214 of FIG. 3) and a corresponding partition unit(e.g., partition unit 220A-220N of FIG. 2). The graphics processingpipeline 3100 may also be implemented using dedicated processing unitsfor one or more functions. In one embodiment, one or more portions ofthe graphics processing pipeline 3100 can be performed by parallelprocessing logic within a general purpose processor (e.g., CPU). In oneembodiment, one or more portions of the graphics processing pipeline3100 can access on-chip memory (e.g., parallel processor memory 2822 asin FIG. 28) via a memory interface 3128, which may be an instance of thememory interface 2818 of FIG. 28.

In one embodiment the data assembler 3102 is a processing unit thatcollects vertex data for surfaces and primitives. The data assembler3102 then outputs the vertex data, including the vertex attributes, tothe vertex processing unit 3104. The vertex processing unit 3104 is aprogrammable execution unit that executes vertex shader programs,lighting and transforming vertex data as specified by the vertex shaderprograms. The vertex processing unit 3104 reads data that is stored incache, local or system memory for use in processing the vertex data andmay be programmed to transform the vertex data from an object-basedcoordinate representation to a world space coordinate space or anormalized device coordinate space.

A first instance of a primitive assembler 3106 receives vertexattributes from the vertex processing unit 310. The primitive assembler3106 readings stored vertex attributes as needed and constructs graphicsprimitives for processing by tessellation control processing unit 3108.The graphics primitives include triangles, line segments, points,patches, and so forth, as supported by various graphics processingapplication programming interfaces (APIs).

The tessellation control processing unit 3108 treats the input verticesas control points for a geometric patch. The control points aretransformed from an input representation from the patch (e.g., thepatch's bases) to a representation that is suitable for use in surfaceevaluation by the tessellation evaluation processing unit 3112. Thetessellation control processing unit 3108 can also compute tessellationfactors for edges of geometric patches. A tessellation factor applies toa single edge and quantifies a view-dependent level of detail associatedwith the edge. A tessellation unit 3110 is configured to receive thetessellation factors for edges of a patch and to tessellate the patchinto multiple geometric primitives such as line, triangle, orquadrilateral primitives, which are transmitted to a tessellationevaluation processing unit 3112. The tessellation evaluation processingunit 3112 operates on parameterized coordinates of the subdivided patchto generate a surface representation and vertex attributes for eachvertex associated with the geometric primitives.

A second instance of a primitive assembler 3114 receives vertexattributes from the tessellation evaluation processing unit 3112,reading stored vertex attributes as needed, and constructs graphicsprimitives for processing by the geometry processing unit 3116. Thegeometry processing unit 3116 is a programmable execution unit thatexecutes geometry shader programs to transform graphics primitivesreceived from primitive assembler 3114 as specified by the geometryshader programs. In one embodiment the geometry processing unit 3116 isprogrammed to subdivide the graphics primitives into one or more newgraphics primitives and calculate parameters used to rasterize the newgraphics primitives.

In some embodiments the geometry processing unit 3116 can add or deleteelements in the geometry stream. The geometry processing unit 3116outputs the parameters and vertices specifying new graphics primitivesto primitive assembler 3118. The primitive assembler 3118 receives theparameters and vertices from the geometry processing unit 3116 andconstructs graphics primitives for processing by a viewport scale, cull,and clip unit 3120. The geometry processing unit 3116 reads data that isstored in parallel processor memory or system memory for use inprocessing the geometry data. The viewport scale, cull, and clip unit3120 performs clipping, culling, and viewport scaling and outputsprocessed graphics primitives to a rasterizer 3122.

The rasterizer 3122 can perform depth culling and other depth-basedoptimizations. The rasterizer 3122 also performs scan conversion on thenew graphics primitives to generate fragments and output those fragmentsand associated coverage data to the fragment/pixel processing unit 3124.The fragment/pixel processing unit 3124 is a programmable execution unitthat is configured to execute fragment shader programs or pixel shaderprograms. The fragment/pixel processing unit 3124 transforming fragmentsor pixels received from rasterizer 3122, as specified by the fragment orpixel shader programs. For example, the fragment/pixel processing unit3124 may be programmed to perform operations included but not limited totexture mapping, shading, blending, texture correction and perspectivecorrection to produce shaded fragments or pixels that are output to araster operations unit 3126. The fragment/pixel processing unit 3124 canread data that is stored in either the parallel processor memory or thesystem memory for use when processing the fragment data. Fragment orpixel shader programs may be configured to shade at sample, pixel, tile,or other granularities depending on the sampling rate configured for theprocessing units.

The raster operations unit 3126 is a processing unit that performsraster operations including, but not limited to stencil, z test,blending, and the like, and outputs pixel data as processed graphicsdata to be stored in graphics memory (e.g., parallel processor memory2822 as in FIG. 28, and/or system memory 2704 as in FIG. 27, to bedisplayed on the one or more display device(s) 2710 or for furtherprocessing by one of the one or more processor(s) 2702 or parallelprocessor(s) 2712. In some embodiments the raster operations unit 3126is configured to compress z or color data that is written to memory anddecompress z or color data that is read from memory.

In embodiments, the term “engine” or “module” or “logic” may refer to,be part of, or include an application specific integrated circuit(ASIC), an electronic circuit, a processor (shared, dedicated, orgroup), and/or memory (shared, dedicated, or group) that execute one ormore software or firmware programs, a combinational logic circuit,and/or other suitable components that provide the describedfunctionality. In embodiments, an engine or a module may be implementedin firmware, hardware, software, or any combination of firmware,hardware, and software.

Embodiments of the invention may include various steps, which have beendescribed above. The steps may be embodied in machine-executableinstructions which may be used to cause a general-purpose orspecial-purpose processor to perform the steps. Alternatively, thesesteps may be performed by specific hardware components that containhardwired logic for performing the steps, or by any combination ofprogrammed computer components and custom hardware components.

As described herein, instructions may refer to specific configurationsof hardware such as application specific integrated circuits (ASICs)configured to perform certain operations or having a predeterminedfunctionality or software instructions stored in memory embodied in anon-transitory computer readable medium. Thus, the techniques shown inthe figures can be implemented using code and data stored and executedon one or more electronic devices (e.g., an end station, a networkelement, etc.). Such electronic devices store and communicate(internally and/or with other electronic devices over a network) codeand data using computer machine-readable media, such as non-transitorycomputer machine-readable storage media (e.g., magnetic disks; opticaldisks; random access memory; read only memory; flash memory devices;phase-change memory) and transitory computer machine-readablecommunication media (e.g., electrical, optical, acoustical or other formof propagated signals—such as carrier waves, infrared signals, digitalsignals, etc.).

In addition, such electronic devices typically include a set of one ormore processors coupled to one or more other components, such as one ormore storage devices (non-transitory machine-readable storage media),user input/output devices (e.g., a keyboard, a touchscreen, and/or adisplay), and network connections. The coupling of the set of processorsand other components is typically through one or more busses and bridges(also termed as bus controllers). The storage device and signalscarrying the network traffic respectively represent one or moremachine-readable storage media and machine-readable communication media.Thus, the storage device of a given electronic device typically storescode and/or data for execution on the set of one or more processors ofthat electronic device. Of course, one or more parts of an embodiment ofthe invention may be implemented using different combinations ofsoftware, firmware, and/or hardware. Throughout this detaileddescription, for the purposes of explanation, numerous specific detailswere set forth in order to provide a thorough understanding of thepresent invention. It will be apparent, however, to one skilled in theart that the invention may be practiced without some of these specificdetails. In certain instances, well known structures and functions werenot described in elaborate detail in order to avoid obscuring thesubject matter of the present invention. Accordingly, the scope andspirit of the invention should be judged in terms of the claims whichfollow.

What is claimed is:
 1. An apparatus comprising: a graphics processingunit (GPU) comprising a plurality of graphics processing resources;slice configuration hardware logic to logically subdivide the graphicsprocessing resources into a plurality of slices; and slice allocationhardware logic to allocate a designated number of slices to each virtualmachine (VM) of a plurality of VMs running in a virtualized executionenvironment, the slice allocation hardware logic to allocate differentnumbers of slices to different VMs based on graphics processingrequirements and/or priorities of each of the VMs.
 2. The apparatus asin claim 1 wherein the slice allocation hardware logic is to allocateslices to VMs based also on a defined quality of service (QoS)associated with each of the VMs.
 3. The apparatus as in claim 1 whereinthe slice allocation hardware logic is to detect changes to thepriorities and/or graphics processing requirements and responsivelyreallocate slices to VMs in accordance with the changes.
 4. Theapparatus as in claim 1 wherein a slice comprises a specified set ofexecution units, data ports to a connection fabric, shared local memory(SLM), set of samplers, pixel back-end resources, 3D graphics processingengines, media encode/decode engines, and/or rasterization hardware. 5.The apparatus as in claim 1 further comprising: a plurality of monitorsto monitor VM-aware signals at one or more stages of a pipeline of theGPU; and a plurality of statistics counters to count events associatedwith the VM-aware signals.
 6. The apparatus as in claim 5 wherein theVM-aware signals comprise data packets which include VM identifiers toidentify each packet as being associated with a particular VM.
 7. Theapparatus as in claim 6 further comprising: a report generator to reportvalues of the statistics counters out to specified regions of memoryassociated with each VM.
 8. The apparatus as in claim 1 furthercomprising: a graphics scheduler to schedule operations on graphicsengines of the slices; a first producer engine associated with a firstslice to provide an indication to the graphics scheduler that it hasdata to be used by one or more consumer engines; a first consumer engineassociated with the first slice or a second slice to provide anindication to the graphics scheduler that it is available to receive thedata.
 9. The apparatus as in claim 8 wherein the graphics schedulercomprises multiple sets of buffered coalescing registers, each setassociated with a different VM, wherein the indication provided to thegraphics scheduler by the first producer is to be coalesced with one ormore prior indications provided to the graphics scheduler.
 10. A methodcomprising: logically subdividing a set of graphics processing resourcesof a graphics processing unit (GPU) into a plurality of slices; andallocating a designated number of slices to each virtual machine (VM) ofa plurality of VMs running in a virtualized execution environment,wherein different numbers of slices are allocated to different VMs basedon graphics processing requirements and/or priorities of each of theVMs.
 11. The method as in claim 10 wherein the slices are allocated toVMs based also on a defined quality of service (QoS) associated witheach of the VMs.
 12. The method as in claim 10 further comprising:detecting changes to the priorities and/or graphics processingrequirements; and responsively reallocating slices to VMs in accordancewith the changes.
 13. The method as in claim 10 wherein a slicecomprises a specified set of execution units, data ports to acommunication fabric, shared local memory (SLM), set of samplers, pixelback-end resources, 3D graphics processing engines, media encode/decodeengines, and/or rasterization hardware.
 14. The method as in claim 10further comprising: monitoring VM-aware signals at one or more stages ofa pipeline of the GPU; and counting events associated with the VM-awaresignals with a plurality of statistics counters.
 15. The apparatus as inclaim 14 wherein the VM-aware signals comprise data packets whichinclude VM identifiers to identify each packet as being associated witha particular VM.
 16. The apparatus as in claim 15 further comprising:reporting values of the statistics counters out to specified regions ofmemory associated with each VM.
 17. The apparatus as in claim 10 furthercomprising: providing an indication by a first producer engineassociated with a first slice to a graphics scheduler that the firstproducer engine has data to be used by one or more consumer engines;providing an indication to the graphics scheduler by a first consumerengine associated with the first slice or a second slice that the firstconsumer engine is available to receive the data.
 18. The apparatus asin claim 17 wherein the graphics scheduler comprises multiple sets ofbuffered coalescing registers, each set associated with a different VM,wherein the indication provided to the graphics scheduler by the firstproducer is to be coalesced with one or more prior indications providedto the graphics scheduler.
 19. A machine-readable medium having programcode stored thereon which, when executed by a machine, causes themachine to perform the operations of: logically subdividing a set ofgraphics processing resources of a graphics processing unit (GPU) into aplurality of slices; and allocating a designated number of slices toeach virtual machine (VM) of a plurality of VMs running in a virtualizedexecution environment, wherein different numbers of slices are allocatedto different VMs based on graphics processing requirements and/orpriorities of each of the VMs.
 20. The machine-readable medium as inclaim 19 wherein the slices are allocated to VMs based also on a definedquality of service (QoS) associated with each of the VMs.
 21. Themachine-readable medium as in claim 19 further comprising program codeto cause the machine to perform the operation of: monitoring VM-awaresignals at one or more stages of a pipeline of the GPU; and countingevents associated with the VM-aware signals with a plurality ofstatistics counters.
 22. The machine-readable medium as in claim 21wherein the VM-aware signals comprise data packets which include VMidentifiers to identify each packet as being associated with aparticular VM.
 23. The machine-readable medium as in claim 22 furthercomprising program code to cause the machine to perform the operationsof: reporting values of the statistics counters out to specified regionsof memory associated with each VM.
 24. The machine-readable medium as inclaim 19 further comprising program code to cause the machine to performthe operations of: providing an indication by a first producer engineassociated with a first slice to a graphics scheduler that the firstproducer engine has data to be used by one or more consumer engines;providing an indication to the graphics scheduler by a first consumerengine associated with the first slice or a second slice that the firstconsumer engine is available to receive the data.
 25. Themachine-readable medium as in claim 24 wherein the graphics schedulercomprises multiple sets of buffered coalescing registers, each setassociated with a different VM, wherein the indication provided to thegraphics scheduler by the first producer is to be coalesced with one ormore prior indications provided to the graphics scheduler.